Reconstruction of the 2003 Flood, using Multi-resolution and Multi-temporal satellite imagery

Oinam Bakimchandra January, 2006

Reconstruction of 2003 Daya River Flood, using Multi-

resolution and Multi-temporal Satellite Imagery by

Oinam Bakimchandra

Thesis submitted to the International Institute for Geo-information Science and Earth Observation in partial fulfilment of the requirements for the degree of Master of Science in Geo-information Science and Earth Observation, Specialisation: Hazard and Risk Analysis

Thesis Assessment Board Thesis Supervisors:

Chairman: Prof. Dr. Freek van der Meer, ITC Dr. V. Hari Prasad, IIRS External Examiner: Dr. S.K.Jain, NIH, Roorkee Drs.Dinand Alkema, ITC IIRS Member : Dr. S.P. Aggarwal,IIRS Mr.G.Srinivasa Rao, NRSA IIRS Member : Dr.V.Hari Prasad, IIRS

iirs

INDIAN INSTITUTE OF REMOTE SENSING (NATIONAL REMOTE SENSING AGENCY) DEPARTMENT OF SPACE, DEHRADUN, & INTERNATIONAL INSTITUTE FOR GEO-INFORMATION SCIENCE AND EARTH OBSERVATION ENSCHEDE, THE NETHERLANDS

Dedicated to MY BELOVED MOTHER & FATHER

I certify that although I may have conferred with others in preparing for this assignment, and drawn upon a range of sources cited in this work, the content of this thesis report is my original work.

Signed …………………….

Disclaimer

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

Abstract

Floods are a common disaster in many parts of the world. It is considered to be the most common, costly and deadly of all natural hazards. Flooding is not just confined to certain region of the world but is a globally pervasive hazard. India experiences one of the highest incidences of Flood, and the area subjected to it is estimated to be one-eight of the geographical area (0.410 M km2) and have been occurring almost regularly each and every year.

Among the several states prone to flooding, Orissa state is one of them, which is mainly due to the existence of delta system. Studies have been carried out related to understand dynamic processes and the flooding problem in Mahanadi river system in the past. In this study, Daya river system, which is a part of Mahanadi Delta and which is one of the most flood prone areas in Orissa is selected. Heavy monsoon rainfall and congestion of drainage pattern in the river system are considered to be the main cause of flooding. Moreover, one of the main causes of flooding in this area is due to low and high tide (tidal effect), from the Chilka Lake.

With advances in the field of Remote sensing and GIS technology makes possible to monitor and study various natural and environmental hazards in a large spatial extent using a long time-series data, which in turn provide a means for quick response, recovery and mitigation activities during and after any natural calamities.

In this study, an attempt has been made to reconstruct the Daya Flooding event, 2003, which help in understanding the dynamic of Daya river system. Since flood is a dynamic phenomenon, the period of submergence vary greatly spatially. To capture and analyse the 2003-flooding phenomenon, which took place during 28th August 2003 to 20th September 2003, multi-sensor (optical and microwave data) and multi-temporal satellite data before and during the flooding period were acquired. Two broad approaches are adopted and implemented which integrates the information extracted using remote sensing techniques, for inundation mapping and GIS approach to determine the maximum extent of flooding and to overcome the constraint of temporal resolution in the application of satellite images in flood inundation mapping, with a strong historic and geomorphic data that in turn helps in understanding the dynamic and the flooding pattern prevalent in this region.

From historical data analysis, it is concluded that regarding the necessity of having proper and accurate historical information’s, which will provide a complete picture in understanding the real phenomenon of the event under consideration. In this study, past 40 years flood event maximum discharge data at various locations are analyzed and it is concluded that Daya River flooding event of 2003, is the third highest flood in magnitude with a return period of 14 years. It’s also found that a large magnitude flooding has a low exceedence probability when compared with event having less discharge. Moreover, the trend in flooding pattern is also studied and observed that whether there is any deviation in the trend observed.

In Remote sensing approach, various techniques of extraction of flooded extent have been performed on multi-temporal RADARSAT imagery of 4th, 11th, 13th and 20th September 2003 and various optical datasets i.e. IRS-Pan, LISS-III and Aster of different dates which help to analyse the inundation

i pattern extracted from various datasets i.e. visual interpretation and automated digital techniques. A comparative analysis of inundation extent extracted is made. Various digital classification techniques such as supervised, unsupervised, thresholding/density slicing, textural analysis based classification and Principal component analysis based classification were explored which help to find a quick, accurate method for flood mapping which can be made operational in future. Analysis from optical dataset by visual interpretation gives quite a reliable and stable inundation extent. The extent of inundation from visual interpretation of dB RADARSAT image (50 m resolution) is taken as a reference extent for determining the variation in spatial extent; due to its high contrast which inturn help in clear identification of land-water boundary extent. Percentage inundation in the reference images of 4th, 11th, 13th and 20th September 2003 are 42.78 %, 50.31%, 38.86% and 36.03%, where maximum and minimum flood extent is observed on 11th September and 20th September 2003 respectively.

Considering the maximum flood extent of 11th September, the variation in spatial extent observed by different digital techniques is presented here. By thresholding, for the dB, DN images are about 7.71% (in case of 50m,dB); 2.89% (50 m, DN) and - 11.01% (100 m, DN). This indicates low variation in extent for 50 m dB and DN as compared with that of 100 m DN. Similarly, variation in extent by unsupervised technique for the three dataset on 11th September gives about 7.29 %( in 50m,dB); 2.89% (in 50 m,DN) and 3.45% (in 100 m,DN). Inundation extent given by this technique is more than that of extent by visual interpretation for all the three dataset. Less deviation of the extent is observed from 100 m, DN as compared with the 50 m images. In case of supervised classification, variation in extent is observed when two different classifiers are applied i.e. maximum likelihood classifier (MLC) and minimum distance (MD) to mean classifier. The inundated map from the supervised classification varies to a large extent. For 50 m, dB variation of 0.48% and 18.45 % is observed from the resulted map by Maximum likelihood and Minimum distance to mean classifier. In case of the other two dataset, percentage deviation in extent observed is 50% and 26.36% (in case of 50 m, DN); 0.577% and 28.47% (in case of 100 m, DN). This clearly indicates the accuracy of extent generated by MLC as compared with MD, considering the deviation observed in 50 m, dB and 100 m, DN images. In Principal component analysis, the extent given by principal component 1(dB image) is deviated by 5.35% and that of 50 m, dB and DN Principal component 1 images by 15.52% and 35.26% respectively. It’s also highlighted in this study regarding the importance of colour composition for accurate feature identification and extraction. A colour composite of PC1:PC3:PC1; PC3, PC3, PC1 gives a distinct land-water boundary which help to visualise and extract the inundation extent accurately. Simple thresholding technique comes out to be the best reliable and suitable automatic techniques for quick inundation mapping accurately. Applicability of textural based classification for automatic flood inundation extraction is also attempted here. It’s observed that textural measures such as Homogeneity, Contrast and Second Moment come out to be the best suitable measures for extracting flood extent which is comparable with that of reference visually interpreted results of 50m, dB dataset. In 50 m db image, a deviation of 0.498%,-3.32% and 0.102 % could be observed when comparing the homogeneity, contrast and second moment derived inundation extent with that of reference extent of September 11th 2003. Similarly, in case of 50 m DN deviation observed was 0.626 %, -3.24% and 0.201%; and in 100 m DN the deviation is -34.906%, -49.49% and -26.56% respectively. It could be inferred form this study that dataset of 50 m dB and DN gives more accurate extent as compared with that of 100 m DN.

ii The percentage inundation obtained is about 49.57%, 48.68% and 49.75% from IRS-Pan (5.8 m), Pan- sharpened LISS-III (5.8 m), IRS-1C (LISS-III, 23.5 m) respectively for same date i.e.08-9-2003, which shows less variation of extent within the optical dataset. Hence, variation in extent using multi- sensor dataset is highlighted in this study.

In GIS based approach, attempt was made to generate accurate DEM from Aster Epi-polar images, field contour map to developed Cost-distance grid i.e. least accumulation cost- distance matrix, which is then used to integrate with the inundation map derived from RADARSAT to get the inundation extent map that correspond with the peak flood discharge. The existing DEMs i.e. public domain Aster DEM, DEM generated using filed contour map could not generate cost-distance matrix in Arc GIS environment. Hence maximum inundated extent could not be generated in this study. This reflects the necessity of an accurate DEM for flood related studies.

Keywords: Delta, Multi-temporal satellite data, Multi-sensor satellite data, historical data analysis, texture-based classification, Homogeneity, Contrast, Second moment, Epi-polar images, cost-distance grid, dB value, DN value, RADARSAT imagery

iii Acknowledgements

Sometimes “Thanks” are but a humble expression of the deep debt of gratitude which one’s feels in one’s heart but since there is no other word which can better express one’s feeling of gratitude than this. I must have recourse to it and express my deep debt of gratitude to my Supervisor Dr.V.Hari Prasad, I/C Water Resource Division, Indian Institute of Remote Sensing, Dehradun, who first mooted me the idea of work on such a rewarding topic.

Gifted with technical acumen, he has all along been guided me with genial keenness and benign interest. I thank him for his able guidance. My supervisor, Dr. V.Hari Prasad, deserves my sincere thanks in this venture of mine. Being an able guide in my work, he has always been quite considerate & obliging. He has very kindly spared as much time as I needed for supervision of my work.

I am thankful to my ITC supervisor, Drs.Dinand Alkema, ITC, The Netherlands for all the effort and thought put into this research. More importantly, I thank him for all the guidance, support, and time spent reading various versions of this study. Whatever little has been done is to a large measure due to his relentless & useful criticism & constructive suggestions from time to time.

Moreover, I would like to show my gratitude to Mr. G.Srinivasa Rao, DSC, NRSA for his input and support at the initial stage of this study. I also thank him for giving me the privilege to work at NRSA during the Fieldwork stage and appreciate the staff and resources associated with this organization.

Again, I am greatly beholden to Dr.V.K.Dadhwal, our able & kind Dean, Indian Institute of Remote Sensing who has given me an opportunity to undertake this work in his institute as a student of this institute & for his kind & able guidance throughout the period of stay at IIRS, Dehradun.

I gratefully acknowledge the Orissa Remote Sensing Application Centre, Flood cell, Bhubaneshwar; Power and Irrigation Department, ; for providing and helping me in collection of data’s in completing this project. I duly acknowledge Er.Ambuja Nayak, Assistant Engineer, Irrigation Department, Bhubaneshwar for his kind support, help and co-operation during the tenure of my field work at Orissa. Moreover, I acknowledge Mr. Ammar Hussein, Managing Director, Chevron Steel Private Ltd., Mumbai for his financial support during the course of study.

I am also thankful and grateful for the opportunity that the Indian Institute of Remote sensing and ITC has given me over the last two years. It’s truly has been an honour to study and work with such a high calibre of faculty and staff both at IIRS and ITC. Also, my experiences in Enschede and Dehradun would not have been nearly as memorable or pleasant without all the love, support and intellect of my fellow graduate students both at IIRS and at ITC. I could not have asked for better friends.

Not the least I must thank to my Parents who bore the financial burdens for this work & for their loving and caring support in every aspect of life. Finally, without the blessing of my Mother to provide me with a clear mind and the support of my near and dear one’s none of this would have ever been possible. I cannot express how much you all mean to me.

Oinam Bakimchandra Dated: January 2006 Dehradun

iv Table of contents

1. Introduction ...... 1 1.1. General Introduction...... 1 1.2. Relevance of this study...... 3 1.3. Research Objectives: ...... 4 1.3.1. Main Research Objective: ...... 4 1.3.2. Sub-Research Objectives:...... 4 1.4. Research Question:...... 5 1.5. Hypothesis:...... 5 1.6. Organization of the Thesis (Schematic representation):...... 6 2. Description of the Study area and its Flooding problem...... 7 2.1. Characteristic of the study area (,Orissa)...... 7 2.1.1. Background- (Location) ...... 7 2.1.2. Problem in and around the Study area...... 9 2.1.3. Rainfall ...... 11 2.1.4. Drainage system and the water level (gauge level) of the river ...... 12 2.1.5. Population density and socio-economic ...... 14 2.1.6. Geomorphology and Landuse/Landcover of the study area...... 15 2.2. Flooding scenerio and their causes...... 17 2.2.1. Insight to Historical Data approach...... 17 2.2.2. Flooding and Geomorphic units...... 19 2.2.3. Anthropogenic contribution- Man induced changes and their relation with flooding .20 3. Literature Review...... 21 3.1. General concept of Flooding ...... 21 3.2. Global perspective- Flooding ...... 22 3.3. Floods in Indian Context ...... 23 3.4. Remote sensing and Flood inundation mapping ...... 25 3.4.1. Microwave remote sensing for inundation mapping ...... 27 3.4.2. General review on inundation mapping by Remote sensing and GIS Technology....31 3.5. Previous Flood related studies on Mahanadi River network (focusing on 2003 Flood event) 32 3.5.1. Study on Evolution and Dynamic processes- ...... 33 3.5.2. Uncertanities of DEM for Hydraulic modeling...... 34 3.5.3. Inundation mapping and Damage assessment using Microwave data...... 34 3.5.4. Flood inundation mapping and 1-D Hydrodynamic Modelling using Remote sensing and GIS technique ...... 35 4. Materials and Methods ...... 37 4.1. Data Acquisition...... 37 4.1.1. Remotely sensed data ...... 37 4.1.1.1. Sensor characteristics of the Datasets...... 40 4.1.2. Data from field/ other source...... 42 4.1.3. Digital elevation model ...... 45 4.2. Methods...... 48 4.2.1. General Overall Methodology...... 49

v 4.2.1.1. Remote sensing approach...... 49 4.2.1.2. GIS based approach...... 50 4.2.2. Software used: ...... 51 4.2.3. Application of Different techniques for Flood inundation extent mapping ...... 51 4.2.3.1. Visual Interpretation...... 51 4.2.3.2. Thresholding/ Density slicing ...... 52 4.2.3.3. Unsupervised classification...... 53 4.2.3.4. Supervised classification ...... 54 4.2.3.5. Principal component analysis...... 55 4.2.3.6. Textural analysis based classification...... 55 4.2.4. GIS Methodology...... 57 4.2.4.1. Generation of least accumulative cost-distance surface matrix and obtaining the Maximum inundation extent corresponding to actual peak flooding...... 58 4.2.4.2. Execution the technique in ARCGIS 9.0 Platform...... 60 5. Results and Discussion...... 63 5.1. Field Data Analysis ...... 63 5.1.1. Historical Data Analysis...... 63 5.1.1.1. Recurrence interval and exceedence probability analysis for 40 years Peak Flood discharge 63 5.1.1.2. Determination of occurrence of Flood in Past 40 years ...... 66 5.1.1.3. Determination of time shift between the highest flood level and the acquisition date of RADARSAT imagery and that of Optical imagery (ASTER, IRS-PAN and LISS-III)68 5.1.2. Geomorphological analysis- geomorphic units inundation extent determination ...... 71 5.2. Remote Sensing Data Analysis ...... 78 5.2.1. Visual Interpretation...... 78 5.2.1.1. Analysis of Multi-temporal and Multi-resolution RADARSAT imagery...... 78 5.2.1.2. Analysis of inundation extent extracted from IRS-1C/1D Satellite imagery ...... 80 5.2.1.3. Comparison of inundation extent from Optical and RADARSAT imagery...... 81 5.2.1.4. Inundation Extent comparison between RADARSAT and ASTER imagery ...... 82 5.2.2. Digital analysis and extraction of Inundation area ...... 83 5.2.2.1. Thresholding approach...... 85 5.2.2.2. Unsupervised approach ...... 88 5.2.2.3. Supervised approach...... 91 5.2.2.4. PCA approach...... 94 5.2.2.5. Textural based approach...... 100 5.3. GIS Approach Analysis...... 109 5.3.1.1. Generation of Cost-distance raster ...... 109 5.3.2. Extraction of Maximum inundated extent...... 109 6. Conclusion and Recommendation...... 110 6.1. Conclusions ...... 110 6.2. Limitation of the Research ...... 112 6.3. Recommendation...... 112 Reference:...... 113 Appendix:...... 117

vi List of figures

Figure 1-1 Cause of Floods and Flood intensifying factors (Ward and Robinson, 2000)...... 1 Figure 1-2 Schematic representation of the Thesis ...... 6 Figure 2-1 Map showing location of Study area ...... 7 Figure 2-2 A schematic network showing drainage distribution of the Lower Mahanadi system...... 10 Figure 2-3 (a) Map showing Daya and Permanent water bodies (b) # Map showing locations of Escapes channel and River adjoining Daya...... 10 Figure 2-4 RADARSAT Satellite imagery showing River network in Lower Mahanadi (Orissa).....12 Figure 2-5(a) Geomorphology map of the study area which indicating the various geomorphic units (b) Extent/location of the geomorphological unit w.r.t. its surrounding areas ...... 16 Figure 2-6 Land use/ Land cover Map of the study area (using Maximum Likelihood classifier).....17 Figure 3-1 Cartographic Flowchart used to Monitor & detect potential flood inundation areas ...... 27 Figure 3-2 Image Mode available in RADARSAT-1...... 29 Figure 3-3 Mahanadi River Network (showing Daya River and other tributaries of Mahanadi River network) ...... 33 Figure 4-1 RADARSAT SAR (dB image, 50m) acquired on 4 and 11 September 2003 ...... 38 Figure 4-2 RADARSAT SAR (dB image, 50m) acquired on 13 and 20th September 2003 ...... 39 Figure 4-3 Pre-Flood LISS-III imagery acquired on 16-01-2003 (FCC) and ASTER acquired on 21- 09-2003 (yellow boundary indicates the study area extent in ASTER scene...... 39 Figure 4-4 Schematic diagram of Daya River system (inflow to Chilka Lake) ...... 43 Figure 4-5 Distribution of Flood Discharge in Lower Mahanadi System...... 44 Figure 4-6 Map showing canal drains and poor drainage area...... 44 Figure 4-7 TIN Model Figure 4-8 DEM generated from Field Map ...... 45 Figure 4-9 Steps involved in automatic generation of DEM using ASTER (VNIR) in Geometica v.9.1.7...... 46 Figure 4-10 Automatic Extracted DEM (many failure surface can seen on the image which is represented by dark areas)...... 47 Figure 4-11 Mosaic DEM as obtained from EOS Data gate way (http://edcimswww.cr.usgs.gov/pub/imswelcome/) ...... 47 Figure 4-12 Two embankment breach as observed in IRS-PAN (5.8m)...... 48 Figure 4-13 General Methodology (combination of Historical, RS, GIS Approach) ...... 49 Figure 4-14 A schematic outline of Remote sensing approach...... 50 Figure 4-15 Schematic outline of GIS approach ...... 50 Figure 4-16 Thresholding of September 4th RADARSAT imagery (dB value, 50m)...... 52 Figure 4-17(Left) Co-occurrence texture measures computation window and (right) one of the resulting texture image ...... 57 Figure 4-18 Temporal Relationship between flooded area, flood event and Radarsat Observation. ...58 Figure 4-19 A Schematic diagram showing integration of RS and GIS Approach using least accumulative cost distance matrix...... 60 Figure 5-1 Flood frequency curve for past 40 years ...... 64 Figure 5-2 Relationship between the discharge and exceedence probability (1/Tr) ...... 65 Figure 5-3 Trend in Peak Flood discharge level in 40 years time-period ...... 65 Figure 5-4 Trend in Flood stage level (gauge reading) for 40 years time-period at Naraj Gauging site ...... 66

vii Figure 5-5 Time shift between highest flood level and the flood situation registered by the satellite at Madhipur Gauging site...... 69 Figure 5-6 Time shift between highest flood level and the flood situation registered by the satellite at Kanas Gauging site...... 69 Figure 5-7 Time shift between highest flood level and the flood situation registered by the satellite at Kanti Gauging site...... 70 Figure 5-8 Showing Flood wave propagation during the time of data acquisitions...... 70 Figure 5-9 Trend of geomorphic units inundated on each date...... 73 Figure 5-10 Geomorphic units inundated on September 04...... 73 Figure 5-11 Geomorphic units inundated on September 11...... 74 Figure 5-12 Geomorphic units inundated on September 13...... 74 Figure 5-13 Geomorphic units inundated on September 20...... 74 Figure 5-14 Flood Evolution map showing the propagation of flooding pattern derived using multi- temporal RADARSAT imagery ...... 75 Figure 5-15 Pattern of inundation in each geomorphic unit...... 77 Figure 5-16 Visual interpreted inundation map of Multi-temporal RADARSAT imagery ...... 79 Figure 5-17 Visual Interpretation Map of Optical Datasets ...... 80 Figure 5-18 Inundation areal extent (in km2) as extracted out from 3 different dataset...... 86 Figure 5-19 Threshold based classified Inundation Map of multi- temporal RADARSAT 50 m, (dB) ...... 87 Figure 5-20 Threshold based classified Inundation Map of multi-temporal RADARSAT50 m,(DN)87 Figure 5-21 Threshold based classified Inundation Map of multi-temporal RADARSAT100 m,(DN)88 Figure 5-22 Classified inundation map of Multi-temporal RADARSAT 50 m, dB...... 89 Figure 5-23 Classified inundation map of Multi-temporal RADARSAT 50 m, DN...... 89 Figure 5-24 Classified inundation map of Multi-temporal RADARSAT 100 m, DN...... 89 Figure 5-25 Dynamic trend of flooded areas from three different dataset ...... 90 Figure 5-26 Distribution of inundation extent pattern in the three dataset using MLC...... 92 Figure 5-27 Distribution of inundation extent pattern in the three dataset using Minimum Distance.92 Figure 5-28 Classification results from Maximum Likelihood classifier (MLC) and Minimum Distance (MD), as applied to three different multi-temporal datasets...... 93 Figure 5-29 PCI of Multi-temporal RADARSAT imagery (50 m, dB image)...... 95 Figure 5-30 Spectral profile of inundated area and non-inundated areas present in different PCI-50 m, dB image ...... 95 Figure 5-31 PCI of Multi-temporal RADARSAT imagery (50 m, DN image)...... 96 Figure 5-32 Spectral profile of inundated area and non-inundated areas present in different PCI-50 m, DN image ...... 97 Figure 5-33 Spectral profile of inundated area and non-inundated areas present in different PCI -50 m, DN image ...... 98 Figure 5-34 Final output Flood map representing the maximum inundated area as generated form PC analysis...... 99 Figure 5-35 Textural images for September 11th 2003 (50 m,DN) ...... 103 Figure 5-36 Textural classified image for September 11th 2003, (50 m,dB)...... 103

viii List of tables

Table 2-1 General information on study area...... 8 Table 2-2 Detail information on study area...... 8 Table 2-3 Rainfall distribution of Puri district for last 3 years, 2001-03 ...... 11 Table 2-4 Danger level of major rivers and highest gauge reading recorded along Daya River ...... 14 Table 2-5 Area of Population Density of Puri District...... 15 Table 2-6 Past recorded Flood event and their statistics ...... 18 Table 2-7Area under each geomorphic unit (Sq.Km) ...... 20 Table 3-1 Flood Classification System ...... 24 Table 3-2 Basic information of RADARSAT Data (different modes available in RADARSAT)...... 28 Table 3-3 RADARSAT –SAR Characteristics...... 28 Table 4-1 Brief Description of Imageries used in the Study ...... 38 Table 4-2 Characteristics of IRS-1C PAN and LISS-III ...... 40 Table 4-3 Standard Flood Discharge Level Classification...... 43 Table 5-1Occurrence of different magnitude of Flood in 40 year time-period (descending order).....66 Table 5-2 Geomorphic units inundated on each dates...... 72 Table 5-3 Inundation areal extent pattern on each geomorphic units (in km2) ...... 76 Table 5-4 Inundation extent from RADARSAT imagery ...... 79 Table 5-5 Comparison of percentage inundation interpreted visually from optical dataset...... 80 Table 5-6 Comparison of inundation extent by visual interpretation of optical and RADARSAT images...... 82 Table 5-7 Area of inundation extent in ASTER and RADARSAT...... 83 Table 5-8 Comparison chart of inundation extent for RADARSAT imagery(Scan SAR Narrow, 50 m, dB)...... 83 Table 5-9 Comparison chart of inundation extent for RADARSAT imagery (50, DN value)...... 84 Table 5-10 Comparison chart of inundation extent for RADARSAT imagery (100, DN value)...... 84 Table 5-11 Threshold range for all the multi-temporal and multi-resolution RADARSAT ...... 86 Table 5-12 Inundation extent by Iso-data Clustering...... 90 Table 5-13 Inundation extent obtained from supervised approach ...... 91 Table 5-14 Statistics of the input bands from Multi-temporal RADARSAT 50m, dB image ...... 96 Table 5-15 Eigen vector matrix of Multi-temporal RADARSAT imagery 50 m,dB dataset...... 96 Table 5-16 Statistics of the input bands from Multi-temporal RADARSAT 50m, DN image ...... 97 Table 5-17 Eigen vector matrix of Multi-temporal RADARSAT imagery 50 m, DN dataset...... 97 Table 5-18 Statistics of the input bands from Multi-temporal RADARSAT 100m, DN image ...... 98 Table 5-19 Eigen vector matrix of Multi-temporal RADARSAT imagery 100 m, DN dataset...... 98 Table 5-20 Areal extent given by each component when applied to Multi- resolution and Multi- temporal dataset...... 99 Table 5-21 Comparison of inundation extent by applying threshold to original and Textural measures image ...... 102 Table 5-22 Thresholding Range -Textural Analysis for Flood inundation extent determination- 50m (dB) 50m(DN) 100m(DN)...... 104 Table 5-23 Multi- temporal RADARSAT ( 50 m,dB value ) -Textural Analysis for inundation extent ...... 105

ix Table 5-24 Multi- temporal RADARSAT ( 50 m,DN value ) -Textural Analysis for inundation extent ...... 106 Table 5-25 Multi- temporal RADARSAT ( 100 m,DN value ) -Textural Analysis for inundation extent ...... 107

x RECONSTRUCTION OF 2003 DAYA RIVER FLOOD, USING MULTI-RESOLUTION AND MULTI-TEMPORAL SATELLITE IMAGERY

1. Introduction

1.1. General Introduction

Floods are a common disaster in many areas of the world. Floods related disaster do not confine themselves exclusively or even primarily to riverine floods. Floods may come in the form of “flash floods” which come with little or no warning. Other floods are more gradual, as with a large storm front, a tropical storm, or a hurricane etc. Others natural hazards like Earthquake and volcanic eruption can produce landslides that causes flooding by damming rivers. From a natural hazards perspective view there exists an important similarity between river flooding, lake flooding, flooding resulting from poor drainage in areas of low relief, and flooding caused by storm surges, tsunamis, avalanches, landslides and mudflows: To a certain extent all are hazards controlled, by the local topography and to a varying degree it is possible to determine the hazard-prone areas. Generally, Floodwaters often cause an extensive damage to property and life; pollute the sanitary drinking water systems, making them unsafe to use. In a year when heavy rain coincides with seasonal high water, property which has not been exposed to flooding for years or even decades may be threatened.(IFAS, 1998)

Figure 1-1 Cause of Floods and Flood intensifying factors (Ward and Robinson, 2000)

1 RECONSTRUCTION OF 2003 DAYA RIVER FLOOD, USING MULTI-RESOLUTION AND MULTI-TEMPORAL SATELLITE IMAGERY

The natural flow of a river is variable and flooding is a natural and recurring event for a river or a stream. The level at which the high level flows become floods is a matter of perspective. Any relatively high stream flow overtopping the natural or artificial banks in any reach of a stream is known as flood. But there are different perspectives to define floods- from a pure ecological perspective; floods are over bank flows that provide moisture and nutrient to floodplain. From a geomorphic perspective, high flow becomes floods when they carry/transport large amount of sediment or alter the morphology of the river channel and the flood plain. From a human perspective, high flow becomes floods when they injure or kill people, or when they damage means of livelihood. In general, flooding is a result of heavy or continuous rainfall exceeding the absorptive capacity of soil and the flow capacity of rivers, streams and coastal areas. Figure 1.1 highlights the potential causes of floods as well as various intensifying factors. Flood events can occur due to a wide range of both natural and human induced elements, the most common obviously being severe and prolonged precipitation. Furthermore, there exist a number of factors that can further affect the process of flooding. Such factors can be human and physical, and will exert dominant controls to either intensify or ameliorate an event. The floodplain is generally considered the region, which is most prone to flooding and hazardous for the development of activities, if the vulnerability of those activities exceeds an acceptable level. Also the definition of the floodplain can be looked at from different perspectives: Topographically, it has been defined as a flat topographic category lying near the stream, geomorphologically, it is a landform composed primarily of adjacent depositional material derived from sediments being transported by the related stream; hydrologically, it can be defined as a landform subjected to periodic flooding by a parent stream. A combination of these perhaps comprises the essential criteria for defining the floodplain. Generally, floods are described in term of their statistical frequency i.e. a 100-year flood or 100-year floodplain describes an event or an area subject to a 1% probability of a certain size flood occurring in any given year. Since floodplain can be mapped, boundary of 100 years floodplain is commonly adopted in floodplain mitigation programs for identification of region, which has significant risk to flooding. Any other statistical frequency of flood event can be used depending upon the degree of risk that is selected for evaluation purpose. The frequency of inundation depends on the climate, the material that makes up the banks of the stream, and the channel slope, where substantial rainfall occurs in a particular season each year, or where the annual flood is derived principally from snowmelt, the floodplain may be inundated nearly each year, even along large streams with very small channel slopes(USDE, 2000).

Floods are among the most common, most costly and most deadly of the natural hazards. The damage caused by floods in term of loss of life, property and economic loss due to disruption of economic activity are all too well known. It’s not possible to control flood completely, however, its extent and damages could be minimized by proper flood control measures. Generally, various activities tend to concentrate in flood prone areas, which in turn results in greater flood damages. It’s required to undertake various measures which encompass a wide range of activities i.e. a long and short term prediction, prevention, warning, monitoring and relief along a floodplain regulations, if losses due to flood in term of lives and property are to be minimized. To carry out the above task/measures there is a need of interaction among different government and private agencies on one hand, and the people of the region facing the disaster on the other hand. The effective flood risk management of rivers (fluvial risk management) needs to consider the whole process from the point at which rainfall first hits the ground to the place where it is finally discharged to the sea. This often requires a full understanding of how the physical process works but also an understanding of how human activities like urban

2 RECONSTRUCTION OF 2003 DAYA RIVER FLOOD, USING MULTI-RESOLUTION AND MULTI-TEMPORAL SATELLITE IMAGERY

development and agriculture can change it. Flood risk management must primarily evaluate the risk to people but must also consider technical and economic reasons for intervening whilst taking environmental issues into account too. Managing floods can often have a significant impact on the people living within a community and above all, effective flood risk management needs the co- operation and support of the people who will benefit from it. Flood defences can have a significant impact on both the natural and built environment and do not always offer a simple solution to the problem. While they may reduce the risk of flooding in one area, they can increase the risk elsewhere. Hence, a Sustainable flood risk management can only be achieved by working with the natural responses of the river basin. Floods can only be managed, not prevented, and the community must learn to live with rivers.

1.2. Relevance of this study

It’s no accident that a flood is the pivotal force of nature in the bible, or that it gained such a grip on the imagination of man, including that of scientists. Flood myths are common to all societies. Floods are most frequent and the most lethal of all natural disasters. They account for 40 percent of all such deaths, a fact that tallies with a second important fact: more than half the world’s population lives on sea coast, in river deltas or along the estuaries and river mouths. It has always been calamitous certainly; but they have also been bringers of new life.

The environmental and economic importance of major floods and droughts emphasizes the need for a better understanding of hydrometeorologic processes and of related climatic and hydrologic fluctuations or variability. In the United States, the average annual flood damage for the 10-year period 1979-88 was $2.4 billion and the average annual number of deaths for the period 1925-88 was 95 (Jarrett, 1991).

The most rapidly growing Third world Flood disasters are caused by humans making their land more prone to floods and themselves more vulnerable. According to figures from the US office for Foreign Disaster Assistance (USOFDA), floods affected 5.2 million people per year in the 60’s compared with 15.4 million in the 1970’s-an almost threefold increase. Over 1964-82, floods killed 80,000 people and affected 221 million worldwide. In 1983, there were major floods in Bangladesh, China, India, Nepal and Papua New Guinea; there was also flooding in Argentina, Bolivia, Cuba, Ecuador, Paraguay and Peru. In the same year the league of Red Cross and Red Crescent societies launched eight international appeals to assist a total of 1.6 million flood victims in five Latin American nations.

Generally a time-instantaneous portrait of a Flood stage over a wide area is made possible due to Remote sensing Technology. But since flood is a dynamic phenomenon, the period of submergence may vary greatly at different place and take times from hours to weeks. This in turn leads to incapability of mapping the widest spread of flooding (time delay between the peak flood phase and that of satellite observation.)

In India one can generally state that flooding occur during the monsoon season, where there is large amount of rainfall. So, the greatest problem is the inability of the optical sensors to mapped/imaged earth surface during cloudy condition. This in turn, makes it difficult to map the spatial extent of

3 RECONSTRUCTION OF 2003 DAYA RIVER FLOOD, USING MULTI-RESOLUTION AND MULTI-TEMPORAL SATELLITE IMAGERY

inundation. On the other hand microwave data/radar image have a limitation in difficult classification of acquired signal because of influence of complex ground and system variables.

So an integrated approach of combining all data source & critically capture flood extent for response, recovery and mitigation activities during and after a flood event have to be developed. Combined with exogenous and historical data like River gauge reading for past peak flood discharge, written records on past Flood event & records from archives related to Flooding phenomenon, within a GIS platform could provide first hand information for flood prevention decision making (Tholey et al., 1997).

Moreover, understanding the morphology and hydrological characteristic of the region would be a complementary effect to conclude whether a specified Flood event is induced due to anthropogenic or natural causes. Also it would help to identify the real causes of inundation at various part/region of study area and will gives an idea whether it’s due to local rainfall, river flooding or tidal effect from sea, in case of Coastal areas.

Difficulty arises with the interpretation of flood extent from single image for a long duration floods. Normally, flood does not occur at a same time and location in region of complex topography, but its occurrence depends on upstream reach and downstream reach. Although for damage assessment and design purposes, maximum inundated areas are needed, and that information are gathered from images acquired closer to peak flooding. So, determining a maximum inundated flood map is necessary.

1.3. Research Objectives: The main research and sub-research objective, which is addressed in this study, pertaining to the Daya Flood event of 2003, Orrisa (India) are as follows:

1.3.1. Main Research Objective:

“Re-construction of the 2003 Daya flood event using historic and geomorphologic data in addition to multiple source and multi-temporal satellite data.”

1.3.2. Sub-Research Objectives:

To develop a methodology to map the full extent of inundation when satellite data is acquired some day’s after actual flood event, thus overcoming the problem of temporal resolution. To define and establish a relationship that will give the most optimal pixel resolution for extracting the full extent of inundation by integrating multi-sensor datasets. To analyse the geomorphologic and historic data of the proposed area to understand the dynamics of the river course. To identify an appropriate GIS approach for data integration and presentation of the results.

4 RECONSTRUCTION OF 2003 DAYA RIVER FLOOD, USING MULTI-RESOLUTION AND MULTI-TEMPORAL SATELLITE IMAGERY

1.4. Research Question:

Is it possible to obtain the Flood’s maximum extent from multi-source and multi-temporal satellite imagery? What is the variation in flooded spatial extent using various satellite data by remote sensing & GIS approach? Can additional topographic, historic and geomorphologic data improve the accuracy of the flood extent assessment? Can we obtain an impression of the dynamics of the Daya River, based on written historical records & available hydrological and geomorphologic data?

1.5. Hypothesis:

“It’s possible to determine the maximum extent of flood inundation of a region using multi- sensor & multi-temporal approach.”

“Additional data sources, like historic records and geomorphologic analysis will give better insight in the dynamics of the river system, and will thus improve the reconstruction of the 2003 flood event.”

“A relationship between satellite image resolution & their corresponding inundation map extent can be established.”

5 RECONSTRUCTION OF 2003 DAYA RIVER FLOOD, USING MULTI-RESOLUTION AND MULTI-TEMPORAL SATELLITE IMAGERY

1.6. Organization of the Thesis (Schematic representation):

The schematic representation of the whole thesis is represented as in figure below:

Primary Context

Literature Review Detailed information on study area Understanding Sensor characteristics -Optical; Microwave

Methodology development

Remote sensing approach Historical data approach GIS Approach

Integration of Remote sensing & GIS Approach

Methodological approach

Input Data Data integration and Analysis Data processing

*Expected Results/outcome *Future Research recommendation

Figure 1-2 Schematic representation of the Thesis

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2. Description of the Study area and its Flooding problem

2.1. Characteristic of the study area (Puri district,Orissa)

Puri is one of the districts under Orissa state. Generally known as, the abode of Lord Jagannath, it is situated in the eastern part of Orissa and is one of the four holy dhams of Hinduism. Puri is also called "Sri Purusottama Dham" or "Martya Vaikuntha", the abode of Lord Vishnu on earth. It is located at a distance of 60 kms from the state capital Bhubaneshwar. The study area is concentrated in and around Daya River which lies between 19 ° 48 ' 33.08 " N to 20 ° 13 ' 12.33 " N latitude and 85 ° 28 ' 57.55 " E -- 85 ° 52 ' 0.66 " E. The location of study area is as shown below:

Figure 2-1 Map showing location of Study area

2.1.1. Background- (Location)

The District of Puri, the holy land of Lord Jagannath is located in the coastal track of Orissa. Its boundaries extend in the north to , in the south to the and Ganjam District, in the west to Khurda District and in the east to the Bay of Bengal. The entire Puri District is covered with plain alluvial track and the coastal belt mainly utilised for its high fishery potentiality. It has a sprawling beach line of about 150 Kms. The District enjoys a tropical climate with an average rainfall of 1586.1 mm, most of which comes during the months from June to October.

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Generally, continuous rains in the upper catchments of the Mahanadi river systems adjoining the State of and local rains led to Floods in Orissa. Heavy rainfall during last part of August and first part of September 2003 created an unusual situation, ultimately leading to flooding, in almost all district of Orissa i.e. 23 district inundated and affected, by reinforcement of floodwater from Hirakud and upper catchments. The District is in deltaic zone of Mahanadi system with major rivers like Kushabhadra, Bhargabi, Daya and Devi with tributaries and distributaries of small rivers, such as Luna, Makara, Rajua, Prachi, Dhanua, Ratnachira and Kadua. The network of rivers spread through the district with geographical disadvantage makes the district flood-prone and thus the district experiences flood at regular intervals. About 40% of the floodwater of Mahanadi System is drained out through this district by the rivers to the and Bay of Bengal. Moreover, about 4.4 % of total Flood discharge from Naraj Gauging site is reported to receive at Daya river system. In addition, since there was accumulation of rainwater in the rivers, there was apprehension of severe flood due to flow of floodwater from Hirakud. General information about Puri District is summarised as below in the Table 2-1 & Table 2-2:

Table 2-1 General information on study area

Area 3,051 Sq.Km. Forest 137.10Sq.Km. Population 14,98,604 Literacy Rate 78.40% Head quarter Puri Vidhansabha seats 6 Sub division 1 Villages 1,714 Blocks 11 Grama panchayat 204 Municipality 1 Towns 4 N.A.C 3 Temperature 36.2(Max), 13.3(Min) Tahasils 5 Rainfall 1586.1mm(Avg) Source: http://www.orissa.net/links/DistrictInfo/Puri.asp

Table 2-2 Detail information on study area

1 Total Geographical Area 3,475 Sq.Km. 2. Total Cultivated Area 1,88,745 hects. 3. Total cropped Area 1,51,065 hects. Paddy 1,28,200 hects. Non-paddy 22,865 hects. 4. Major crops a) Kharif 1,51,065 hects. b) Rabi 1,51,300 hects. 5 Total population (2001 Census 14,98,604 Provisional) a) Male 7,61,397 b) Female 7,37,207 6 Percentage of Population a) Schedule Caste 18.56 b) Schedule Tribe 0.27

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c) Small Farmers 33.87 d) Marginal Farmers 53.86 e) Other farmers 12.25 7 Total No. of Farmers 1,492,94 8 Total Live Stock Population a) Buffalo 21905 b) Cows 434321 c) Sheep 45853 d) Goats 94797 e) Pigs 1147 f) Poultry 295561 9 No. of Police Stations 17 10 No. of villages 1710 11 No. of Blocks 11 12 No. of Tahasils 07 13 No. of Gram Panchayats 230 14 No. of Municipality / NAC 4 15 No. of Sub-Division 1

Source: Ministry of Water Resources, Water Management wing, Orissa

2.1.2. Problem in and around the Study area

Puri District is in lower deltaic zone of Mahanadi system with major rivers like Kushabhadra, Bhargabi, Daya and Devi with tributaries and distributaries of small rivers, such as Luna, Makara, Rajua, Prachi, Dhanua, Ratnachira and the total length of the drainage in this district is about 2463 km and the drainage density of this district is 0.71 km per Km2(District contingency plan, 2003). It was reported that these rivers and their congestion of drainage pattern are the main cause of flooding in the study area. Moreover, one of the main causes of flooding in this area is due to low and high tide (tidal effect), where there is repulsion of water from the Chilka lake. The schematic flow diagram of the whole drainage distribution of the Lower Mahanadi system is as shown in Figure 2-2.

9 RECONSTRUCTION OF 2003 DAYA RIVER FLOOD, USING MULTI-RESOLUTION AND MULTI-TEMPORAL SATELLITE IMAGERY

Naraj (Gauging site) Kathajori Mahanadi

Kuakhai Kathajori Mahanadi Birupa

Mancheswar Spill Way

Below Spill

DAYA Bhargavi Kushabhadra Madhipur Jogisahi Escape Ramachandrpur Achutpur Escape Kanti Escape Escape Below Escape Below Kanti Below Escape Jalahat Gap

Jakara Gobkund Cut Below Jalahat Chilka Lake Bay of Bengal

Source: Flood cell, Government of Orissa, Bhubaneshwar Figure 2-2 A schematic network showing drainage distribution of the Lower Mahanadi system

Source: # Irrigation and Power Department, Mahanadi Delta Plan, Government of Orissa

Figure 2-3 (a) Map showing Daya and Permanent water bodies (b) # Map showing locations of Escapes channel and River adjoining Daya

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Daya is one of the rivers flowing in Puri district, which have a great potential to Flooding during monsoon season. The whole course/length of the river system has embankment on both side. The left side of the river is well protected i.e. about 45 Km full embankment, as compared to right path, thereby making the left side of the river a safe place against flooding whereas more damage is reported to the right side (western part) of the river which is under Kurda division as reported from 2003 Flood event. Basically, Daya bifurcate and split into two tributaries namely, Daya and Luna River and joined again at the lower reach before inflow/ discharging to Chilka Lake.

2.1.3. Rainfall

Interaction of the basic monsoon flow with the Orography and the synoptic disturbances developing over Indian region are the basic reason for heavy monsoon rainfall event, which in turn causes flood in different part of India. Flood in Orissa mostly occur during monsoon season due to very heavy rainfall caused by the synoptic scale monsoon disturbances. Most of the very heavy rainfall events occur in July and August. Orissa receives during the monsoon (June–October) a rainfall of 1200 mm. It’s been reported that a one-day storm rainfall even up to 500 mm during severe cyclones has been recorded on several occasions (BishnuP.Das, 2005). The regions extending from central part of coastal Orissa in the southeast towards Sambalpur district in the northwest, experience higher frequency and higher intensity of very heavy rainfall with less interannual variability (M.Mohapatra and U.C.Mohanty, 2005).

Excessive rainfall in the Puri district was observed during the later part of August 2003, which led to Flooding in almost all part of Mahanadi River system (Orissa). Due to low pressure in Bay of Bengal, there was heavy rain from 24th to 28th August 2003 in almost all the areas of the District. All the rivers are in its brink and low-lying areas became waterlogged submerging paddy lands. The rainfall occurred during the months of June, July and August for the last 3 years along with the normal (long term average) values are given in Table 2-3. So even for 900 mm of rainfall there was lots of damage due to flood, and with the availability of Multi-Temporal RADARSAT data and other ground information’s on 2003 flood event, the study was proposed to be carried out for 2003. It’s seen from the rainfall distribution pattern that in 2003, the rainfall during the month of June and July was below Normal, but in August the intensity of rainfall increase as compared to normal i.e.577 mm as compared with normal rainfall of 300.3 mm.

Table 2-3 Rainfall distribution of Puri district for last 3 years, 2001-03

MONTH Normal YEAR (mm) 2001(mm) 2002(mm) 2003(mm) June 207.00 492.90 121.30 134.00 July 310.08 530.18 142.00 246.02 August 300.30 241.27 378.18 577.00

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2.1.4. Drainage system and the water level (gauge level) of the river

MAHANADI RIVER NARAJ GAUGING SITE

K R U I V S E H R A B H A R D E R IV B A R H

ORISSA A A D AY R D E G V I A R V IV I E R R

I V E DEVI MOUTH R STUDY AREA PURI KUSHABHADRA MOUTH E AK A L ILK CH BAY OF BENGAL

Figure 2-4 RADARSAT Satellite imagery showing River network in Lower Mahanadi (Orissa)

The Daya is one of the main branches in the Mahanadi delta in Orissa, India. In Orissa, there are five major rivers systems: the Mahanadi, the Brahmani, the Baitarani, the Subarnerakha and the Budhabalanga as shown in Figure: 2-3.All these rivers have their deltaic plains adjoining and overlapping on each other stretching almost 250 km along the coast and 80 km across. This fertile alluvial zone of 20,000 km2 is extremely flat with a gradient of 1 in 5000–10000, and gets affected by floods almost annually, which cause drainage congestion and submersion up to 1 meter (BishnuP.Das, 2005). The largest river Mahanadi draining a basin area of 1, 43, 000 km2 has a deltaic plain of 7000 km2 built up by the main river and its six branches-Birupa, Chitrotala, Kathjuri-Devi, Kusabhadra, Bhargavi and Daya. Two barrages exist - one 100 km upstream of the river and the other barrage 20 km downstream. Water from these two barrages is used for irrigation purpose covering an area of about 3000 km2.

All rivers and tributaries are protected by embankment and in particular the areas of extensive irrigation are protected by High level spills way i.e. escapes built on both the banks of each river channel. This made other areas where there is no protection by spillways vulnerable to flood water.

The management of the lower Mahanadi Delta Rivers and their drainage system over the past 50 years can be summarised as follow:

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Bishnu Das (2005) reported in his paper that in the flattest doabs (area between two deltaic branches i.e. Back swamp areas) covering Kusabhadra-Bhargavi and Bhargavi-Daya rivers, 600 km2 saucer shaped land in close proximity to the coast was remaining perpetually water-logged affected by flood water. The current drainage pattern where draining to the branch by the central drain of Ratnachira canal and to the Kusabhadra river branch by that of central drain Dhanua canal was reported to be infeasible because of locking effect along the main river (Refer Figure: 2-2, 2-3,2-4). The 110 km long, Kuakhai-Bhargavi river branch with a slope of 1 in 10,000 for the final 50 km is unable to discharge into the Chilika lake. To manage and resolve this complex and problematic drainage system in the Mahanadi delta command an innovative solution for the 600 km2 lowland was evolved by providing a 12 km long straight cut to sea from Bhargavi river at the 40th km i.e. a new canal comes up which is named as Gobkund cut, thereby the main river shortened its length by 40 km.(Refer: Fig: 2-2) This direct cut 300 m wide and 2 m deep has been operational for the last 30 years and has diverted up to 2000 m3sec–1 of high flood (almost 70%) directly to the sea. (BishnuP.Das, 2005)

It’s further reported that this cut which shortened the main length of the Bhargavi river by 40 km has been helpful in restoring and sustaining agriculture by improved drainage over 30,000 Ha of totally unproductive ill drained land in the tail reach. The flood level has lowered by 2 m thereby facilitating surface drainage into the river. The straight cut however, has deprived the tail reach of the environmental in stream flow and has itself experienced degradation in its hydraulic regime. Moreover, the river too experienced aggradations below the cut. Hence, the need for improving surface runoff and the need to integrate sub-surface drainage over the entire deltaic stretch of 7000 km2 is recommended in study made by Bishnu P. Das.

The development of Mahanadi delta system is categories into seven different stages. The stages of development is said to have occurred at 26, 18, 15, 9, 6 and 1.5 metre contours according to the elevation of the region. After the seventh stage of delta development i.e. last stage of Mahanadi delta development, there is an upliftment as a result of which three sets of parallel sand dunes developed along the coast. A change in the drainage pattern and in deltaic morphology existed which is mainly due to raised sand ridges, which is developed after the seventh stage of delta development. These parallel sand dunes have the maximum development between the Chilka and the Devi mouth and from there up to north of Dhamra. Several lagoons of varied sizes have been formed. Examples are that of Sar Pata and the Samagara Pata located to the north of Puri. (Webindia, 2002)

All the rivers of the district were flowing above the danger level at most of the places during 2003 Flood event as recorded and observed by various gauge station present along the river system. This recent flood almost engulfed the entire district and it is quite unprecedented and crossed the hallmark of the water level in different rivers.

Some danger level of major rivers and highest gauge reading recorded which have been collected from field visit especially for the month of August and September 2003 are shown in Table 2-4:

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Table 2-4 Danger level of major rivers and highest gauge reading recorded along Daya River

Station Geographic Zero Danger Height of Date of Measured Water Name location Level Level water from Measure Water Level Height/Excess (Lat/Long) (Z.L.) (D.L.) M. Z.L (D.L. - ment (M.W.L) M. water (causing M. Z.L) M. (10.00Hrs inundation/floodi ) ng) Metre. Madhipur 20°−7'- 7.53 11.3 3.765 2/09/03 12.185 0.885 18"N / 5 85º-48´- 6˝E Kanti 20°−8'- 5.85 9.62 3.765 30/08/03 11.785 2.165 6"N/ 85°- 5 46'-9"E Kanas 20°−6'- 0.76 4.75 3.99 06/09/03 5.1 0.35 27"N/85° −38'-27"E Ghoradia ----- 4.56 6.24 1.68 N.A. N.A. N.A.

Balabhadr ----- 1.45 3.3 1.85 N.A. N.A. N.A. apur

Nuagoan ----- 0 1.85 1.85 N.A. N.A. N.A.

# N.A.: Not Available

2.1.5. Population density and socio-economic

As per the Ministry of Water Resources, Water Management wing, Orissa, the total population of Puri district was 1498604 including 761397 male and 737207 female during the year 2001. It’s shown that about 35% of the total district area was under low population density while about 33% of the total area was under medium population density. About 18% and 14% of the total area was under high and very high population density. This population density data had been classified with respect to population per square area and was collected from the census of Orissa government. Basically, agriculture is the main occupations of the large section of the society belonging to Puri district. Generally, agriculture is carried out in a primitive and traditional way in most part of this region. Business and trade is also a way of life for a minor section of the population. Water from various rivers flowing in this areas serve as a major source for irrigating the agricultural lands which help the farmer’s in improving their living conditions with increased agricultural production. On the other hand, being coastal areas, this district face’s natural calamities like floods and cyclone every year, which worsen the socio-economic condition of the people living in these areas. The state

14 RECONSTRUCTION OF 2003 DAYA RIVER FLOOD, USING MULTI-RESOLUTION AND MULTI-TEMPORAL SATELLITE IMAGERY

government in spite of their effort to reduce such natural calamities, various coastal areas continue to be grim against such natural catastrophes. Table 9 shows the area population density of Puri District.

Table 2-5 Area of Population Density of Puri District

Population Density Area (km2) % of the Total Area Low 93928.50 35.27 Medium 87781.19 32.96 High 47334.95 17.77 Very High 37278.97 14.00 Source: Ministry of water resources, Water management wing, Orissa

2.1.6. Geomorphology and Landuse/Landcover of the study area

The term “Geomorphology” has been derived from the Greek root words: geo, “earth”, morphos, “shape”, and logos, “reason”. In simple terms, it is the study of the configuration of the Earth surface. The surface configuration is due to continuous spatial variability of landforms, each of which possesses a distinct morphologic expression, which is again characterised by some clearly defined physical properties of materials, and is a product of some dominant geomorphic processes. (S.R.JOG, 1995) The prime aim is to discriminate the landforms of the past from that of the modern processes. This approach will be helpful in elucidating the geomorphic history of a certain region in term of geologic and climatic control. It’s also well known that to understand and interpret the landscape fully, it’s necessary to understand the geomorphic history of the evolution of that particular region. Geomorphic processes responsible for shaping landforms are the very processes causing, in some cases, problem of varied nature. Flood, which is one of the processes; a proper understanding is necessary to overcome their impact on human life.

Geomorphology map of the study area (.jpeg file extension format) obtained from ORSAC, (Orissa Remote sensing Application centre) during the field work, is being updated in ARCGIS 9.0 platform and a new Geomorphology map is generated after identifying various geomorphic features/unit present in the region which is incorporated with attributes containing various geomorphic unit as per NRIS Code. Younger paleao channel/abandoned channel, Deltaic plain, Buried channel, Natural levee, Back swamp, old coastal plain, paleao beach ridge complex, older mud flat, water bodies, Residual hill, Lateritic upland, pediment, valley filled, younger alluvial plain, denudational hills(large/small) and Inselberg are the main important geomorphic units encountered in the area under study.

15 RECONSTRUCTION OF 2003 DAYA RIVER FLOOD, USING MULTI-RESOLUTION AND MULTI-TEMPORAL SATELLITE IMAGERY

The geomorphology map of the study area, which indicates the various geomorphic units are as shown in Figure 2-5 (a) and the extent/location of the geomorphological unit w.r.t. its surrounding areas is as shown in Figure: 2-5 (b)

MAHANADI RIVER

DAYA STUDY AREA

BAY OF BENGAL CHILKA LAKE

Figure 2-5(a) Geomorphology map of the study area which indicating the various geomorphic units (b) Extent/location of the geomorphological unit w.r.t. its surrounding areas

The region represents a typical setup lying in between the alluvial plain of river Daya to the northwestern side, Deltaic plain to the northeastern and southern part, and large part covering with older mud flat and some part with younger mud flat to the southern part towards the Chilka Lake. Natural levee and paleao-abandoned channel are seen in the southern part of the region. In the western and northern part of Daya, denudational hills, Lateritic upland, pediment and Inselberg are seen whereas in the east, back swamp and abandoned channel are prominent. The alluvial plain which is a part of the Daya lies to the north –west direction and it indicate direct influence of Mahanadi River over Daya River. During the monsoons season i.e. July and August the system of abandoned channels and back swamps become active and is fed by overland flow and flood water. During the other season it becomes swampy and water logged without fresh water and precipitation inflow.

Land use practices and Land cover categories are directly related to geomorphic units. So, after analysing the geomorphology of the area, a Landuse –Landcover analysis of the study area is made; in and around Daya River which indicates presence of sandy/dry waste area and extensive agricultural

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area’s. Forest (Degraded), Coconut plantation/ scrub, Upland with/without scrub, Urban Built Up, Water bodies are other land use- land cover features present in this region. The Land use and Land Cover Map prepared from Pre-Flood January 16th 2003, LISS-III (23.5 m) is as shown below in Figure 2-6.

Figure 2-6 Land use/ Land cover Map of the study area (using Maximum Likelihood classifier)

2.2. Flooding scenerio and their causes

2.2.1. Insight to Historical Data approach

A brief description on occurrence of flooding in lower Mahanadi basin based on a report by Flood cell, Bhubaneshwar is analysed and presented herewith: (Floodcell, 2003)

In context to Past Flood event in Orissa, the worst flood of 19th century, one of the first on record, is said to have occurred in October 1834. It was reported in literature that this flooding causes heavy damage in the coastal area including loss of human life and cattle. However, the loss statistics are not found in literatures. The flood discharge during 1866 flood at Naraj was reported to be about 36,342 cumec (12, 83,600 cusecs). An area of about 777 Sq.Km was highlighted to be submerged in Puri district alone and 1662 Sq.Km of area in . About 7 lakh people were affected by this flood event. As per the Mahanadi Basin plan report, Flood during 1896 too caused a heavy devastation in the delta, thereby a breach of about 300m length with 10 m depth of scour were noticed in the embankments. During the flood of 1933, the water level at Naraj is reported to rise to 28 metres with a peak flood discharge of 41,711 Cumecs. Due to this event, the total human loss and cattle is reported to be 8 and 162 lakh respectively. All together it’s reported that 3919 houses were ruined and 7565 houses were damaged in

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Cuttack district. About 39,172 hectare of crop was reported to be destroyed completely while about 6586 hectare was partially damaged. There were flood event reported and observed during 1937, 1955,1980,1982,1991 as per the Mahanadi basin plan report 2003. Of all the events that took place, the number of breach in embankment and the district affected is summarised as below:

Table 2-6 Past recorded Flood event and their statistics

Year of Flood Event No. of embankment District affected Remarks/Additional Breach along the information river course 1937 75 Cuttack,Puri, Nos. of villages Sambalpur affected in Cuttack and Puri district : 758, 556 1955 (July) 263 (* breach at Cuttack, Puri The whole region Daleighai for a width between Mahanadi and of 610 m. Chilka lake looked like almost a sheet of water. About 1086 nos. of villages affected. Population about 14,15,000 were reported to be affected. 1980 92 major breaches; 800 Cuttack, Puri 54 block and 3140 in total. villages were affected in Cuttack and Puri District. Population affected: 20.56 lakhs 1982 420 breach along river Cuttack, Puri and lower Population affected: 5 embankment of Puri Mahanadi Basin million. [flood was so and Cuttack. Area/delta. severe that the historic rivers such as Prachi (silted up about 100 years back) and Alaka (silted up about 200 years back) had opened up during this flood event]

1991 15 nos. of breach along Cuttack and Puri Wide spread damage to Kathjori,Devi, district irrigation distribution Daya,Luna, Kuakhai system. 6.67 lakh etc. hectare of crop is reported to be damaged.

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In this Study, to understand the Flooding scenario and inundation pattern in and around the Daya River system, Historical Data analysis by mean of Flood frequency, and ranking of the flood occurred in past 40 year into three categories i.e. High, Medium and Low is done on the basis of Maximum Flood discharge level obtained from Flood cell, Bhubaneshwor. In addition, Measurement of Water height for the water gauges at Madhipur, Kanti and Kanas from 27th August 2003-24th September 2003 is made, thereby making it possible to ascertain the exact time shift between the highest flood level and the acquisition date of RADARSAT imagery and that of IRS-PAN and LISS-III of September 08 2003. It’s obvious that the acquisition of satellite data has some delay in regards to peak flood.

On the other hand, critical information on topography and landform of the region as a whole which is obtained from ORSAC, Orissa Remote sensing Application Centre in the form of digital geomorphological map, is used as a reference to generate a Geomorphology map of the area for further analysis of geomorphic units. Moreover, many cited literatures related to river engineering and reports of the complex Mahanadi system was referred and obtained from Library, Flood cell, Bhubaneshwor.

Hence, adding the Historical Data analysis approach will help us to suffice and give insight to ascertain whether the flood producing mechanisms in the past have been similar to those of the present scenarios of flooding. Moreover, it can be concluded that studying the past flood event will help us to observe the frequency of occurrence of flood at certain region and to determine whether the flood- producing mechanisms in the past have been same with that of the past or whether there is a deviation in the trend observed.

2.2.2. Flooding and Geomorphic units

As discussed in last section, Geomorphology seeks to identify the regularities among landforms and what processes lead to patterns i.e. its predictability. A natural river seeks to establish a channel morphology which is adjusted to the prevailing hydrologic and sedimentological conditions so that it can continue to carry a wide range of discharges and sediment loads efficiently. However, the natural equilibrium are generally seen to be easily upset by human activities which alter either the catchments and thereby lead to river flooding affecting the surrounding region along the river channel. Hence, the setting of geomorphology and each geomorphic unit has a certain degree of exposure when such flooding event took place. The geomorphological settings of the region have an impact on the inundation pattern visible during flooding period. Some of the Geomorphic units that made up the whole study area are discussed herewith and the exposure of such landform to river flooding. It can be clearly seen from the map in figure? that almost all the part of the study area is predominant with two main kind of geomorphic units i.e. Older Mud Flat and Deltaic Plain. These two units are prone to have a large inundation extent when flood of any magnitude occurs. Younger alluvial plain are also found in and around the river system and is liable to inundated with flooding water. Floodplains are the most dynamic unit that are exposed to flooding at all time. Buried pediments both shallow and medium are two geomorphic setting in the region which is least exposed to river flooding due to excess discharge at the upstream of Mahanadi river system. Lateritic upland, Inselberg being geomorphic settings that are of higher elevated region, the effect due to flooding is minimal. In short, the Geomorphology setting of Orrisa coast as such is mainly depositional in nature.

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Mudflat, which constitutes the main part of the study area, is basically a wide expanse of fine-grained soft clay and slit. A part of Chilka lagoon is also present within the extent of our study area. Generally, this lagoon is fed by the freshwater and excess flooded water discharge mainly from Daya-Bhargavi, which is considered to be tributaries of Mahanadi river system. The area covered under each geomorphic unit, which is derived from geomorphic map of the study area, is given in Table 2-7 below:

Table 2-7Area under each geomorphic unit (Sq.Km)

Geomorph Deltaic Flood Channel Alluvial Plain Buried Back Pediment Buried Lateriti ic units plain plain Bar (Younger) Pediment- swamp Pediment c Shallow -Medium Uplan d Area under 238.09 9.637 32.6 0.147 85.6 27.4 0.54 56.8 71.05 each units

Geomorphic Valley Insel Linear Denud Abandon Residu Natural Older Paleao/aband Mudfl units Fill berg Ridge/Dy ational ed al Hill Levee Mud oned at ke Hills- channel Flat channel(young (You Small er) ng) Area under 48.3 2.38 6.69 0.215 15.12 7.5175 0.732 6.355 475# 11.4375 each unit 375

# Older Mud Flat cover large part of the study area.

2.2.3. Anthropogenic contribution- Man induced changes and their relation with flooding

Over the last 2000 years, and especially the last 300 years, man’s activities have had an increasing influence on drainage basins and their constituent channels (DavidKnighton, 2001).Climatic fluctuation was reported to be less during the past 2000 years, but modification of the physical environment due to human interference have led to changes. Hence, frameworks for estimating the effect of man’s activities have to be provided, as it effect is considered to be same as that of climatic changes. It’s widely acknowledge that human’s interference affect floods and flood hazards. Two types of man induced changes are identified – first categories fall within those changes brought about by direct modification to the channel itself i.e. channel phase changes and the second one, which is indirect or land phase changes. Generally, river regulation works like water storage by reservoirs, diversion of water lead to channel phase changes whereas landuse changes i.e. afforestation, deforestation, urbanization, changes in agricultural activities and land drainage lead to indirect land phase changes. For instance, Land use changes in study area can affect the amount of runoff for a given storm and the rapidity with which it runs off. Human occupancy of floodplains i.e. urbanization, increase the vulnerability due to exposure to flood hazards. Deforestation has been credited with causing important increases in the frequency and severity of flooding. On the other hand, Dams, levee and embankment construction and other channel alteration practices affect flood characteristics to a large degree. This type of construction generally causes a false sense of security on the part of the public as it’s failure

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can causes a catastrophic flooding. In next several decades, land use changes will exacerbate flood hazards in a great many watersheds (DavidKnighton, 2001).

3. Literature Review

3.1. General concept of Flooding

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Floods are a natural occurrence and the risk they pose is wide ranging to both people and property, however it is not practically or economically feasible to eliminate all flood risk (Huntingdon and S.MacDougall, 2002). Flood events can be exacerbated by either and in many cases both anthropogenic and physical factors. In physical terms, climate change has a profound influence upon the natural hydrological cycle, and will therefore undoubtedly affect the rate and magnitude of flooding, thus increasing potential risk.

To determine the extent of flooding is an important role of the hydrological research community and provides a vital service to both planners and engineers. One way in which the inundation of floods can be mapped effectively is through the use of remotely sensed data and that of ancillary ground data for validation. Both satellite and aerially derived data allow the study of flood events to be undertaken at a scale and resolution previously deemed unobtainable and therefore offers great potential for both future studies and understanding. Determining the statistical frequency and probability of a given flood events, gathering of hydrologic data from rivers and streams is necessary but a time consuming effort. If such dynamic data are not available for at least twenty years, assessment becomes difficult. As a result, flood inundation and hazard assessment based on direct measurement from stream gauging records become difficult as there is no basis to determine the specific floods levels and recurrence intervals for given events. With the development of remote sensing and computer analysis techniques, now traditional sources can be supplemented with this new method of acquiring quantitative and qualitative flood hazard information. In recent years, satellite technology has become extremely important to provide cost-effective, reliable and critical mechanism for prevention, preparedness and relief management of flood disaster. With the availability of multiple satellite data, it is now possible to monitor flood situation effectively in a particular region, in a spatial context.

3.2. Global perspective- Flooding

A key driving force according to EAO/UNEP study in 1981 states that the yearly increase in flood disasters is the rapid rate of deforestation in the tropics. Tropical forests are disappearing at the rate of 7.3. million hectares per year: 4.2 million hectares a year in Latin America; 1.8 million hectares a year in Asia; and 1.3 million hectares a year in Africa (Sinha, 1998) Reviewing the literature on Watershed management & flood recently, Roy Ward, Geographer at the University of Hull, UK found that early this century soil conservationists believe that if one could only “stop the rain drop where it fall”, one could minimize floods. It has been remarked that South Asia is among the world’s most vulnerable regions to both natural and human made disasters. It is said that in south Asia, one can set the clock by the diurnal rhythm of the floods. The people of the “Orient” and the Indian sub-continent known from experience that the natural nemesis of “civilized life” is wind, water and tectonic forces. Records of floods on the Danube date from A.D. 1000. In China some of the world's most disastrous floods have been caused by the unstable Huang He (Yellow River). The river, which flows at or above the level of the bordering land, is contained in part by levees; however, because its channel has gradually become filled with deposited sediment, any appreciable increase in its volume causes the river to overflow and flood the surrounding area. The Netherlands, dependent on its dikes for protection from inundation, has suffered many disastrous floods from the sea and the Rhine and Meuse rivers. In 1970, 1985, and 1991, hundreds of thousands of people in Bangladesh were killed when the combination of high tides and a

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tropical cyclone (i.e. Hurricane) storm surge caused widespread flooding of the low-lying delta of the and Bharmaputra rivers (ColumbiaUniversityPress., 1999) Earth Observatory, Natural Hazards section (NASA) reported the occurrence of floods in different region/part of the world recently causing damage to life and property. Some of the recent floods from around the world are as listed below: Flood in Kabul: A blistering heat wave settled over southern Asia for much of June. The high temperatures melted mountain snow packs, sending torrents of flood water down the rivers of Afghanistan, Pakistan, and India dated 21-06-2005. Flood in southern China: Seasonal rains during June 2005 have resulted in widespread flooding across southern and eastern China. The floods and associated mudslides have left hundreds dead and forced thousands from their homes, with the most severe damage in Guangxi and Guangdong in southern China. The rains are a normal part of life in southern China, where May and June are Meiyu season. Meiyu literally means “plum rain,” which refers to the widespread rains that can occur at the time when plums ripen. Summer flood in China: The summer monsoon pounds southern China with heavy rain year after year, often triggering deadly floods. The rainy season officially started on June 1, and by June 5, the media reported 204 flood-related deaths throughout China for the year. The floods started on May 31 when torrential rain caused flash floods and mudslides and have continued through June 9, 2005, Flood in Bangladesh: Flash floods poured over the Khasi Hills after heavy unseasonal rains. On May 27, 2005, when the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA’s Terra satellite captured the top image, most of the northeast corner of Bangladesh was covered with dark blue flood water. To the east, rivers in India are also swollen. According to news reports, 100,000 people have been affected by the floods, and 10 have died. Springtime flood in Southern Russia occurred on 7-05-2005. And at the same time, as of May 5, 2005, 154 people have been reported dead in the wake of severe flooding along the Wabe Shebele in Southeastern Ethiopia. The United Nations Office for the Coordination of Humanitarian Affairs reports that at least 100,000 people have been affected by the flooding (NASA, 2005). The floods started on April 23 when the Wabe Shebele burst its banks. Ongoing rain has prolonged the flooding. Moreover, report has been posted about flood in other part of the world like in Iraq, Algeria, and Greece, Turkey etc The Skew disaster mathematics i.e. the global statistics indicates a significant higher frequency of natural disasters in developing countries relative to the industrialized world (NASA, 2005).

3.3. Floods in Indian Context

Flooding is not just confined to monsoon Asia but is a globally pervasive hazard. India experiences one of the highest incidences of natural disasters in the Southeast Asian countries. The area subjected to flood is estimated to be one-eight of the geographical area and floods have been occurring almost regularly each and every year in different part of the country.

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Flood problem in India is mostly confined to the states located in the Indo-Ganges plains, in Northeast India. But they also occur occasionally in the rivers of central India. This was being concluded from a study made by analysing past 15 years floods in different rivers systems of India (O.N.Dhar and Nandargi, 2003)

There are about 120 major and medium rivers in India which criss-cross the Indian area from north to south and east to west. The numbers of minor rivers that flow over different parts of the country is legion. Indian rivers are classified into 4 major groups on the basis of meteorological, geological and topographical conditions in the context of floods i.e. system, Ganga River system, North West river system and Central India and Deccan river system. In India, a river is said to be in flood when its water level crosses the Danger level (D.L.) at that particular site. Generally, Danger level are usually 1 meter above the warning level (W.L.) According to O.N.Dhar and S. Nandargi (2003) Flood classification system based on Danger level is given in Table 3-1 below:

Table 3-1 Flood Classification System Sl.No. Water level above Danger Nature of Flood Level (Metre) 1 >=1m Major Flood 2 >=5m Severe Flood 3 >=10m Devastating Flood

Source: (O.N.Dhar and Nandargi, 2003)

As such intense rainfall is the main cause responsible for causing floods in India. Major flood producing synoptic weather situations are that of cyclonic disturbances, ‘Break’ monsoon situation, El-Niño and La-Nina phenomena and due to man made factors like indiscriminate destruction of forests and vegetal cover in the upper reaches of river basins over a long period of time; improper agricultural practices; and obstructions to free flow of water in rivers. Sometimes floods are caused by events other than rainfall like coastal floods, floods caused by dam failure, estuarine floods and floods due to snow or glacier ice melting.

Floods of various magnitudes occur in India every year mainly during summer monsoon season. Floods in Indian rivers for the period of 1986-2000 due to summer monsoon season have been studied and analysed by Dhar et.al, 2003. It has been found out that during the summer monsoon season, the passage of tropical disturbance like cyclonic storms, depressions, low pressure areas etc are responsible for causing heavy rainfall which result in causing floods in the different rivers of the country.

Dhar et.al (2003) remarked that the frequency of floods in past 20 years does not depend upon the magnitude of monsoon rainfall as a whole. A monsoon season having large negative percentage departure of rainfall from the normal may record the same number of floods as the one having large departure of rainfall, as in case of 1987 and 1988. The highest numbers of floods were recorded during 1998 flood season of which 136 were categorised under major flood event. The worst affected states by floods are Assam in the Brahmaputra basin, Bihar in the Ganga basin and states of Uttar Pradesh and West Bengal to some lesser extent. It has also been reported about a very few floods in the

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Peninsular India. The Testa flood of 4th October, 1968 at Anderson Bridge in North Bengal is still considered to be the highest recorded flood in India which stood at 18.10 m above Danger Level. Next to it, the Narmada floods of 6th September, 1970 at Gardeshwar in Gujarat which was 17.87 m above Danger Level were some of the most severe floods of the country (O.N.Dhar and Nandargi, 2003).

About 60% to 80% of flood damages occur in the States of Uttar Pradesh, Bihar, West Bengal, Assam and Orrisa. Assam, West Bengal, Bihar, Uttar Pradesh (in Brahmaputra and Ganga basins) and parts of Orrisa experience extensive devastation due to floods frequently than other part of India. It is observed that on an average about 8.6 mha of land area in the country is annually affected due to floods resulting in average annual damages of the order of Rs. 2500 crores including damages to crops, houses and public utilities (Seth, 1998) In India, the Decision Support Centre (DSC), NRSA is providing updated information on Flood Disaster management during last two decades.

3.4. Remote sensing and Flood inundation mapping

Remote sensing is the science and art of obtaining information’s about an object, area, or phenomenon through the analysis of data acquired by a device and it has provided a new impetus for the earth, resource and environmental scientists. During 1960’s when Remote sensing was first coined (Cohen, 2000) it simply referred to the observation and measurement of an object without touching it. Gradually in 1970s the new subject of remote sensing changed in both content and organization. Application of remote sensing techniques for flood related studies has received considerable attention especially during the last decade all over the world. Timely flood monitoring and its impact assessment have regularly been provided on operational by various research organisations like National Remote Sensing Agency (NRSA), NESAC, and department in various part of the world. Generally, the potential uses of remote sensing technology for flood disaster management can be as follows (DSC, 2004): i) Flood inundation mapping and monitoring ii) Rapid and scientific based Damage Assessment iii) Monitoring and mapping of flood control works and changes in river course iv) Identification of river bank erosion v) Identification of chronic flood prone areas vi) Inputs for flood forecasting and warning models.

A system has been developed in China, (Jiqun Zhanga et al., 2002) which is known as NPOIS (National Professional and Operational Integrated System) where it is being used to performed and – i) calculate the distribution of area at high risk by comparing historical flood heights with digital elevation model data and ii) to estimate social and economic losses under different alternatives for decision making or flood routing, based on social and economic databases and corresponding models; (iii) to suggest - the best alternatives for population withdrawal from areas at risk; and (iv) also suggest the best alternatives for storing and transporting flood-prevention materials. The system developed has been targeted to perform the above mentioned task before a Flood. In spite of all the above task, Dynamic monitoring of flooded areas; estimates the expansion of flooded areas according to meteorological and hydrological forecasting and optimizing the transport of materials for disaster relief is being performed by NPOIS.

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Remote sensing technology can be especially useful and desirable when applied during the planning process. It has emerged as a powerful tool to prepare flood inundation maps in near real-time, which can be effectively used for damage and relief management (USDE, 2000). Remote sensing (RS) techniques, e.g. aerial photographs, imaging radar or altimeter (either laser or radar) are becoming very promising tools for determination of water level and the extent of flooded areas (Portmann Felix, 1997). The flood inundation map helps the decision makers to make a scientific assessment and for better management of relief activities. On the other hand, based on satellite data acquired during flood, pre-flood and post flood along with the ground information/ancillary data, flood damages can be estimated. Based on the duration of flooding, magnitude of the flood, number of flood waves, area affected, types of land use features etc. flood damage map can be prepared. But application of this technology does not solve problems, but it provides a planning study with recent, historical, and repetitive information.

In addition, Remote sensing come out to be an cost-effective and efficient technique when large areas are to be covered and analysed for intensive study (Carter, 1982). Satellite data analysis for flood inundation mapping is generally carried out by visual interpretation or by digital image processing methods using a computer. The visual interpretation gives a reasonable accurate assessment of water spread directly from the satellite images. In digital analysis, the computer classifies each pixel into water and non-water categories by comparing their individual reflectance value with the low reflectance of water bodies in the infra-red region. In the infrared region of the Electromagnetic (EM) spectrum, water body absorbs the incoming energy whereas it’s surrounding land features’ including vegetation reflects highly. This unique physical principle of reflectance i.e. difference in signatures profile of different land cover, is used to monitor and map flood inundated area in floodplain very accurately.(Sanyal and X.X.Lu, 2003).Information on flood inundated area for different magnitudes of floods can be provided by Remote sensing so that the extent of flooding can be related to the flood magnitude. Duration of flooding can be estimated in view of multiple coverage of the same area within 3/4 days by satellites. Utilizing optical remote sensing data, the mapping and monitoring of flooded terrain in hilly areas are often difficult and not accurate due the following three reasons. First, delineation of the land water interface in the visible bands becomes a difficult task. Second, the most important, cloud cover is often associated with the meteorological conditions that result in local flooding. Flooding and flood crest also occur under cover of night. Third, varying degrees of vegetation canopy closure can obscure the flood boundary. Total cloud cover, darkness or vegetation canopy means a loss of information from the visible or near infrared portion of the electromagnetic spectrum.

A general methodology to estimate Flood affected Areas and Flood Evolution pattern study is as shown in Figure 3-1 below:

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General Methodology to Estimate Flood affected Areas and Flood Evolution

Satellite Imagery

Optical Data SAR data

Pre-Flood During Flood Post Flood

Digital Image Processing Georectification & Resampling Filtering - Density Slicing - - Textural analysis - - Classification ImImaageg eC lclassificationassification -

Open Flooded areas Permanent water bodies Open Flooded areas

Actual open flooded area

Flood Monitoring Flood evolution

Figure 3-1 Cartographic Flowchart used to Monitor & detect potential flood inundation areas

3.4.1. Microwave remote sensing for inundation mapping

Generally during the floods, it is very difficult to get the cloud free data in such cases, microwave data can be used effectively since, it has penetration capacity through the clouds. Synthetic Aperture Radar (SAR) from ERS or RADARSAT satellite provides this advantage of space imaging in adverse weather conditions. The dynamic difference in roughness characteristics between water (smooth) and land (rough) is apparent on RADAR imagery and makes active microwave an excellent sensor for land/water discrimination. Since 1993, ERS-SAR has been utilized operationally and since 1998, RADARSAT-1 has been utilized operationally in flood monitoring and in mapping. The RADARSAT satellite provides a means of mapping flood extent due to its Synthetic Aperture Radar (SAR) sensor, which penetrates cloud cover and is sensitive to surface structure. As radar discriminates between the smooth water surface and rough land surface, flood areas can be readily detected and mapped. RADARSAT-1 is an advanced Earth observation satellite project developed by the Canadian Space Agency (CSA) to monitor environmental change and to support resource sustainability. SAR sensor of RADARSAT-1 is a microwave instrument, which sends pulsed signals to the Earth and processes the received reflected pulses. RADARSAT is SAR based technology provides its own microwave illumination and thus will operate day and night, regardless of weather conditions. The basic information of RADARSAT Data and RADARSAT SAR characteristics are shown in Table 3-2; 3-3. The RADARSAT satellite has seven SAR imaging options, or beam modes. Each beam mode offers a different area coverage and resolution. RADARSAT imaging options also include collection angles

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from 10° - 59° looking right from ground track. This allows allowing selection of a beam position within each beam mode.(SpaceImaging, 2005).

Table 3-2 Basic information of RADARSAT Data (different modes available in RADARSAT)

Beam Beam Incidenc Nominal Nominal Nominal Mode Positio e angle Resolutio No. of Area n (o) n (m) Looks (km) Fine 10 1x1 50 x 50 F1 37 - 40 F2 39 - 42 F3 41 - 44

F4 43 - 46 F5 45 - 48

Standard S1 20 - 27 30 1x4 100 x S2 24 - 31 100 S3 30 - 37 S4 34 - 40 S5 36 - 42 S6 41 - 46 S7 45 - 49 Wide W1 20 - 31 30 1x4 165 x W2 31 - 39 165 W3 39 - 45 150 x 150 130 x 130 ScanSAR SN1 20 - 40 50 2x2 300 x Narrow SN2 31 - 46 300 ScanSAR SW1 20 - 50 100 2x4 500 x Wide 500 Extende H1* 49 - 52 25 1x4 75 x 75 d High H2* 50 - 53 H3* 52 - 55 H4* 54 - 57 H5* 56 - 58 H6* 57 - 59 Extende L1 10 - 23 35 1x4 170 x d Low 170

Table 3-3 RADARSAT –SAR Characteristics

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As one source of valuable data, RADARSAT offers a number of major benefits including current and reliable access to data, frequent global coverage, range of product scale and resolutions, which can be integrated with other data sets. In addition, the unique features of the RADARSAT sensor provide application specific benefits.

Source: http://www.spaceimaging.com/products/radarsat/index.htm

Figure 3-2 Image Mode available in RADARSAT-1.

As an active sensor, RADARSAT’s synthetic aperture radar (SAR) transmits a microwave energy pulses to the earth. The SAR measures the amount of energy, which returns to the satellite after it interacts with the earth’s surface. Microwave energy penetrates darkness, clouds, rain, dust or haze enabling RADARSAT to collect data under any atmospheric conditions. RADARSAT transmit its C- band microwave energy in a horizontal orientation known as polarization. The energy, which returns to RADARSAT’s sensor, is captured using the same polarization. This is known as a HH polarization system. Variations in the returned signal (backscatter) are a result of variations in the surface roughness and topography as well as physical properties such as moisture content (SpaceImaging, 2005).

Classification of SAR imagery was performed mostly by thresholding techniques, as it provides a good means to obtain an appreciation of the classification errors. Other classification algorithms have also been conceived in some study for classification of SAR imagery like the EBIS algorithm (Evidence Based Interpretation of Satellite images) (Oberstlader et al., 1997). Use of space borne Radar data to model inundation patterns and trace gas emissions in the Central Amazons floodplain has been reported (A.Rosenqvist et al., 2002). It shows how multi-temporal time series of space borne L-band SAR data from Japanese Earth Resource Satellite-1, (JERS-1) were used to generate a model of the spatial and temporal variation of

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inundation on the floodplain of Central Amazon River. It also described how the model is being effectively used for in situ measurement of river stage heights. Moreover, the studied indicated that the map with spatial variation of flood duration on the floodplain is a key factor that controls local variations in plant biodiversity. It’s seen that for such type of work adequate availability of Satellite time series data is the prime factor that would affect the reliability and accuracy of the Flood Models and the spatial details of Flood duration map.

Multi-spectral optical imagery has been used extensively to map wetland region of the world. But presence of dense canopy, however, limits the inability to map in forested wetlands to detect flooding. But with the recent availability of SAR data, it emerges as a useful data source for mapping inundation in forested wetland and ecosystem. It has been reported that L-band radar provides the best distinction of flooding in a forest when compared to C-band radar (Wang et al., 2002). On the other side, GIS database development for modelling floodplain topography were reported to be carried out using ARC/INFO / vector based GIS) or ARC/INFO GRID /raster based GIS)(Philip A.Townsend and J.Walsh, 1998). Moreover, Townsend and Walsh (1998) modelled floodplain inundation through the integration of SAR, geographical information systems (GIS) and optical remote sensing. It was highlighted in the study that Landsat TM data is less appropriate for mapping flood inundation than the tested radar data, as in times of leaf-on trees their use is severely restricted, and best avoided if suitable radar images are available. The synergistic use of radar and optical remote sensing in conjunction with GIS modelling was therefore argued as an effective method for delineating potential inundation in areas of subtle topographic relief. A synergistic approach towards flood inundation mapping was also proposed by Smith (1997), as fixed-frequency SAR was found insufficient for the mapping processes, and thus needed to be combined with visible-infra red data (VNIR). Furthermore, it was found that although SAR is not limited by atmospheric conditions, it was most effective for mapping smooth open water bodies, as emergent vegetation, wind or flow turbulence increased radar back-scatter returns. Nonetheless, it was concluded that SAR provides excellent temporal coverage in certain situations, where it can be used to determine the flood extent through forest canopies and emergent plants (Smith, 1997).

Applicability of Radar images for flood extent mapping despite it’s difficulty in classification, has increased because of their exclusive cloud penetration capacity, thereby offering a primary tools for real time assessment of flooded areas. A land water boundary can be better delineated in Radarsat data and it is easy to classify flood boundaries and inundated extent can easily be estimated in Radarsat data. Radarsat data acquire during flood is integrated with pre-flood as one of the bands and false colour composite can be made. This highlights the flood extent with respect to normal flow configuration of the river, area affected, submerged and non-submerged crop area distinctly. The use of synthetic aperture radar (SAR) and visible/infrared (VIR) satellite imagery for mapping the extent of standing water in the Peace-Athabasca Delta during spring and summer of 1998 was evaluated (Toyra and W.Martz, 2001). It was highlighted in the study about the importance to use Radarsat imagery acquired at low incidence angles. Moreover, classification of the SPOT scene combined with the Radarsat S1 scene achieved significantly better results than those obtained when the SPOT was classified in combination with the Radarsat S7 scene.(Toyra and W.Martz, 2001) Classification and object extraction of radar image are very difficult because it’s imaging mechanism is quite different from multi spectral image. To overcome this difficulties a comprehensive methodology, based on the textural analysis, for flooded area identification from SAR imagery was implemented

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(YangXiaomeiZhouChenghu, 1998). A method of improving classification accuracy using SAR imagery was described by Takashi and Masaharu, 2001. It was found that the best classification was produced by the aggregation of the classified image when using texture images as additional inputs to the classifier. It was also concluded that textural analysis and the aggregation techniques are useful in the classification of SAR images.(Kurosu and Shiyoshi, 2001)

3.4.2. General review on inundation mapping by Remote sensing and GIS Technology

As far as the technical aspect and application of Remote sensing technology for studying flooding phenomenon and its related issues, various literatures have been reviewed. It’s seen that, flood duration map gives the spatio-temporal variations of flooding in the section of the study area and the temporal resolution of flood duration map depends on the satellite repeat cycle e.g. 1 day for NOAA and of the occurrence of gaps in the temporal coverage.

Works have been done towards modelling flood inundation using integrated GIS with Radar and Optical Remote sensing (Philip A.Townsend and J.Walsh, 1998). Derivations of Potential inundation surfaces were made from regression models that relates to known flood elevations to river position and floodplain location. ERS-I and JERS-I images have been used/exploited to identified areas of inundation at different flood levels. Few studies have indicated that the ability to model potential flood inundation and map actual extent of inundation, timing and intensity under different environmental conditions is central key to understand the dynamics of vegetation on the floodplain.

Remote sensing and GIS becomes a common technique for time change analysis. Research on this areas are performed to examine changes in stream morphology and to assess if changes were associated with management, topography or other factors (AndreaS.LaliBerte et al., 2001). It has been concluded that Large scale aerial photography (1:4000) combined with GPS/GIS and ground truthing is ideally suitable & feasible in time change analysis Moreover, cross-classification techniques in GIS is valuable in time change analysis since it can yield areas that have change from land to water and vice-versa. Users perceive changes where they may not be obvious otherwise, with the visual aspects of GIS. Various efficient methods have been developed and employed for flood mapping using remote sensing and GIS technology. Landsat-TM and DEM data were employed to map the coastal floodplain (Wang et al., 2002). Basically, it was based on the reflectance features of water vs. non-water targets on a pairs of Landsat-7 Thematic Mapper (TM) images ( before and after flood event), as well as modelling inundation using Digital Elevation Model data (DEM). Such method is reported to work well in areas of large spatial extent where topography is relatively flat. Studies of mapping flood extent using optical satellite imagery data have noted the inability of the imagery to identify flooded areas under forest cover. SAR (Synthetic Aperture Radar) data which can penetrate cloud cover have been used extensively to mapping flood event of Rhine valley (Oberstlader et al., 1997) and catastrophic flood that occurred in Regione Piemonte in Italy (P.A.Brivio et al., 2002)

Recent studies have indicated that multi-temporal satellite data were used for change detection analysis e.g. generation of flood evolution map i.e. to estimate the trend in flood recession for a certain time period. To look into the discontinuity and trend in the image time series, a suite of relatively

31 RECONSTRUCTION OF 2003 DAYA RIVER FLOOD, USING MULTI-RESOLUTION AND MULTI-TEMPORAL SATELLITE IMAGERY

straight forward standard statistical analysis were complied that in turn were applied to partition the source of variation in a long image time series (Beurs and G.M.Henebry, 2005).

Despite coarse resolution, NOAA (AVHRR) is extensively used for mapping inundated areas and time series analysis of flooding for a particular region. NOAA data have been used to monitor a catastrophic flood occurring over large areas of poor drainage such as flood plains of low gradients and long periods of inundation. However, the presence of clouds limit’s the NOAA data applicability. A algorithm has been proposed and used to recover the cloud covered pixel for flood study (Islam and Sado, 2000) and flood hazard assessment was carried out through NDVI, land cover category and elevation height.(Islam and Sado, 2000) To assess the inundated areas due to flooding, optical sensors data are generally incorporated with detection of vegetation stress and examining the nature of fluvial sediment left on the region (only some days after flood event)

Combination of remote sensing technology and GIS techniques is found to be a very effective tool for water resources development through many studies undertaken recently in these areas. However, it should not be considered as a substitution but a tool for systematically gathering special data which are difficult to get by ground methods and analyzing database that enable to reduce the volume of more expensive exploration works.

3.5. Previous Flood related studies on Mahanadi River network (focusing on 2003 Flood event)

Many studies have been carried out related to dynamic processes and the flooding problem in Mahanadi river system in the past. Many Earth scientist researcher and environmentalist focus on this part of the region because of its high susceptibility to natural calamities like Flooding, Cyclonic storm, which causes suffering and affect people in those region. The Mahanadi River delta plain covers 0.9x 104 km2 and lies between 850 40 : 860 45 E and 190 40 : 200 35 N. Catchments area covers about 1.42 x 105 km2 and the climatic setting is tropical with hot and humid monsoonal climate. The average annual rainfall of the area is about 1572 mm; over 70% is precipitated during the south west monsoon between Mid June to Mid October (Mohanti, 2001). The Mahanadi River starts building up its delta plain from Naraj where the undivided Mahanadi branches forming it’s distributary’s system ramifying in the delta plain area. The Mahanadi river network is as shown in Figure 3-3:

32 RECONSTRUCTION OF 2003 DAYA RIVER FLOOD, USING MULTI-RESOLUTION AND MULTI-TEMPORAL SATELLITE IMAGERY

MAHANADI Naraj Railway Bridge gauge site DEVI

PRACHI HIRAKUND RESERVOIR

KUSHABHADRA DAYA RIVER SYSTEM DAYA DHANUA

BHAGIRATHI

RATNNACHIRA

Source: Flood cell, Government of Orissa, Bhubaneshwar

Figure 3-3 Mahanadi River Network (showing Daya River and other tributaries of Mahanadi River network)

Following sub-sections will give an insight to the work that has been currently carried out related to dynamics of Mahanadi river delta, modelling and inundation mapping of Flood focusing on 2003 flood event.

3.5.1. Study on Evolution and Dynamic processes- Mahanadi river delta

A detailed study on the dynamic processes of Mahanadi river delta was made by (Mohanti, 2001), Department of Geology, Utkal University, Bhubaneshwor. In their study, it’s reported that during the monsoon season, the undivided Mahanadi River at its delta head at Naraj/Mundali carries an annual average discharge of 48,691 million cubic metres of water with a monsoonal component amounting to ca. 41,000 million cubic metres. Floods in the whole network start when water discharge mounts to 17,150 cumsec. A rigorous analysis of variation in daily water discharge during the monsoon season for the period of 2000 to 2003 was made and it reflect the climatic/ metrological fluctuations controlling monsoonal precipitation/ rainfall pattern. Mohanti et.al. (2001) reported that a discharge of 28,580 cumsec may results in damaging floods. Floods may occur more than once in a monsoonal season. It’s concluded that the complex and inter-related factors of global warming/climatic change, feedback of the catchments area, variable monsoonal precipitation influencing sediment supply and transport, tectonics, sea level changes and episodic catastrophic events such as floods, cyclone are judged as a controlling factor for Mahanadi River delta building in time and space in the tropical setting on the east coast of India.

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3.5.2. Uncertanities of DEM for Hydraulic modeling

DEM are of limited quality and therefore uncertainty exists regarding what constitutes appropriate use of a DEM and the validity of DEM-based modelling outcomes. Within the GIS community, there has been an increasing concern about management of the quality of modelling outputs and dealing effectively with uncertainty. Hence, much research work has been focused in this field but despite the increasing concern end users awareness of error, quality and uncertainty remain issues which need to be addressed. Recently a study was carried by Sailesh Kumar, (2005) addressing the flood modelling aspect using Hydro dynamic models like MIKE 11 and SOBEK integrating with Optical and Microwave Remote sensing Imagery. The study reflects and presented different methods for representing error, to quantify uncertainty in DEM (Shailesh, 2005). In his study, RADARSAT SAR images and IRS-1C LISS-III images were used for Flood inundation and modelling purpose for the Flood event that occurred in Orissa in 2003. Modelling was done at a canal side constructed from Kusabhadra river of Puri district, Orissa, India. Availability of SAR helps to monitor the progress of flood and generate a flood maps. Flood inundation scenario were generated using DEMs (ASTER and SRTM) in hydrodynamic models (MIKE 11 and SOBEK) and then it is compared with the flood extent maps derived using the satellite images. In his study, from the observed 5 years gauge data, and that of stage-discharge relationship that was established using observed flood gauge and discharge data, frequency analysis was reported to be carried out. ASTER and SRTM DEM were employed and a comparison of flood inundation scenario was made. He concluded that the DEM contains certain acceptable errors for mapping of Flood Inundation area’s accurately. 1-D modelling using MIKE 11 of the region is reported to be made successfully in his thesis, but failure in case of 2-D modelling using SOBEK, which might be due to greater size of DEM i.e. large data handling problem. His study doesn’t reflect and incorporate a review of the methods of damage assessment.

3.5.3. Inundation mapping and Damage assessment using Microwave data

Inundation mapping and damage assessment of Flood event 2003, Orissa for the Puri District was reported to be carried out as a case study (Rahman, 2004). In his study, RADARSAT SAR and LISS III 1C images were used in flood detection and damage assessment. The main focus in his study is monitoring flood by using multi temporal SAR data and four RADARSAT data (September 04, 11, 13 & 20 of 2003). Inundated areas were extracted from the SAR images by using thresholding methods. Flood damage assessment of the study area was also carried out by overlaying flood-inundated map with the generated land use/ landcover map. The percentage of inundation of each of the land cover classes and each village at the time of flood was presented in his study. He concluded that the combination of microwave and optical satellite data can be utilized advantageously for inundation mapping and damage assessment. Moreover, it will help in quick response to disaster mitigation and management.

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3.5.4. Flood inundation mapping and 1-D Hydrodynamic Modelling using Remote sensing and GIS technique

A similar study with that in section 3.2.; section 3.3 was reported to be carried out in and around Puri District, which main focus was on 1-D hydrodynamic modelling and inundation mapping (Sumangala, 2005). In his study, an attempt was made to develop an integrated methodology for Flood Mapping using combination of RADARSAT, Landsat ETM+ satellite images, ASTER DEM, GIS and Hydrodynamic modelling for the September 2003, Puri Flood event. HEC-RAS was used to generate the longitudinal profile, water level and routed discharge along Bhargavi, Kusabhadra rivers and flood mitigation canal at upstream of Kusabhadra river. Then, Flood Inundation area in and around the study area is reported to be mapped using ARC View GIS with HEC-Geo RAS extension. His results show a promising effect towards integration of GIS, Remote sensing and Hydrodynamic modelling for predicting and mapping flooded areas.

35

RECONSTRUCTION OF 2003 DAYA RIVER FLOOD, USING MULTI-RESOLUTION AND MULTI-TEMPORAL SATELLITE IMAGERY

4. Materials and Methods

The basic information/data set which is needed and required for multi-sensor and multi-temporal analysis for flood inundation mapping is described in the following section below. In addition, the various Methodologies and approach which is adopted for this study is presented in this chapter in details.

4.1. Data Acquisition

4.1.1. Remotely sensed data

Remotely sensed data that are used in this study are IRS-1C/1D LISS-III (23.5m), IRS-Panchromatic (5.8m), RADARSAT SAR (50m, 100m) , and ASTER (15m) Imageries of different dates. IRS -1C/1D LISS III of path-142 and row 36 dated 16th January 2003 (pre-flood) which has 4 bands, with a spatial resolution 23.5 meter and repeativity of 24 days is used for land use/land cover map generation and extraction of permanent Water bodies from the study area. Moreover, it’s used to fuse with IRS- Panchromatic to produce a Pan sharpened image which is having a resolution of 5.8 m. This pan sharpened image help to identify various features present in and around the study area for visual and various digital classification of during and post-flood imageries i.e. RADARSAT imageries of four different dates viz. 4th September, 11th September, 13th September and 20th September 2003;LISS-III of 8th September 2003. The LISS-III imagery of September 08th 2003 is covered and contaminated with cloud which is about 70% of the whole study area. ASTER (15m) of 21st September 2003 which is downloaded from the EOS Data Gateway Site is also covered with cloud which is about 75% of the study area (Refer: Table 4-1)

RADARSAT imageries are obtained from NRSA (National Remote Sensing Agency), Hyderabad. Both the DN values and Backscatter, (dB values-sigma nought) imagery are obtained. Imagery of September 4th, 11th and 13th have a resample pixel spacing of 50 m i.e. Beam mode-scan SAR narrow, and that of September 20th have a pixel spacing of 25 m i.e. Beam mode- extended high. The September 20th SAR imagery is resample to 50 m pixel spacing for further processing and analysis.

Raw data are imported in ERDAS Imagine 8.7 and geometric correction was made i.e. geo referencing and re-sampling. Then, filtering of RADARSAT imagery was carried out by using Lee-Filter, Median Filter to suppress and remove speckle, which in turn improve image quality thereby helping to resolve fine details within the images. Description of co-ordinate system used in all of the maps and images in the study is as follows:

Projection: Polyconic Datum: Everest Spheroid name: Everest

37 RECONSTRUCTION OF 2003 DAYA RIVER FLOOD, USING MULTI-RESOLUTION AND MULTI-TEMPORAL SATELLITE IMAGERY

Longitude of the central Meridian: 84:00:00.00E Latitude of origin of Projection: 17:00:00.00N False Easting at Central Meridian: 0.0000 meters False northing at Origin: 0.0000 meters

Table 4-1 Brief Description of Imageries used in the Study

IRS- IRS-LISS Aster IRS-LISS RADARSAT IRS-PAN Panchromatic III (23.5 m) (15m) III Standard Beam (5.8 m) (5.8 m) (23.5 m) mode: (50m,100m) Pre-Flood Imagery During/Post Flood Imagery 16-01-2003 16-01-2003

04-09-2003 8-09-2003 11-09-2003 8-09-2003 13-09-2003 20-09-2003 21-09- 2003

Figure 4-1 RADARSAT SAR (dB image, 50m) acquired on 4 and 11 September 2003

38 RECONSTRUCTION OF 2003 DAYA RIVER FLOOD, USING MULTI-RESOLUTION AND MULTI-TEMPORAL SATELLITE IMAGERY

Figure 4-2 RADARSAT SAR (dB image, 50m) acquired on 13 and 20th September 2003

Figure 4-3 Pre-Flood LISS-III imagery acquired on 16-01-2003 (FCC) and ASTER acquired on 21-09- 2003 (yellow boundary indicates the study area extent in ASTER scene.

Multi- temporal RADARSAT imageries during peak flood and post flood, dated 4th September, 11th September, 13th September and 20th September and Pre-Flood imagery i.e. IRS-1D LISS-III dated 08- 09-2003 and during flood ASTER 21-09-2003 are as shown in Figure 4-1,4-2 and 4-3.

39 RECONSTRUCTION OF 2003 DAYA RIVER FLOOD, USING MULTI-RESOLUTION AND MULTI-TEMPORAL SATELLITE IMAGERY

4.1.1.1. Sensor characteristics of the Datasets

Since the study involved various multi-temporal and multi-resolution datasets from different remote sensing satellite platform, there is a need of discussing the various sensors characteristics of each datasets. The datasets used here are briefly summarised as below:

a) Panchromatic (mono-spectral, 5.8 m) and LISS-III (MSS, 23.5 m) from IRS-1C

IRS-1C sensors collect data with the different swaths. The swath of LISS-III sensor in the visible bands is 141 km while in SWIR band it is 148 km. The swath of PAN sensors are 70 km. Details of overlaps and sidelaps between scenes of a sensor are given in the table below:

Table 4-2 Characteristics of IRS-1C PAN and LISS-III

Payload Resolution (m) Ground swath Image size Overlap (km) Side lap at (km) (km x km) equator (km) LISS-III Visible 23.5 141 141 x 141 7 23.5 SWIR 70.5 148 141 x 148 7 30 PAN 5.8 70 70 x 70 2 ~1 (Opt) Source: http://www.euromap.de/docs/doc_019.html, accessed on 11-Sep-2005

LISS-III operates in four spectral bands. There is a separate optics and detector array for each band. Three bands (B2, B3 and B4) are in the visible and near infrared region. B5 is in short wave infrared region. Since the first three bands of LISS-III are in the same spectral region as IRS- 1A/1B/P2 sensors, the same nomenclature is continued. Bands B2, B3 and B4 of IRS-1C are therefore identical to that of IRS-1A/1B/P2.

In this study, panchromatic (5.8 m) and LISS-III (23.5 m) imagery of IRS-1C/1D is used which is a pre-flood and during flood imagery acquired on January 16th 2003 and September 8th 2003 respectively. There is a presence of Cloud cover in the during flood imagery which is about 70% of the study area (both in panchromatic and LISS-III of September 8, 2003). of A pan sharpened LISS-III imagery having spatial resolution of the 5.8 m (6m) is also generated using the image fusion technique by PCA method in ERDAS Imagine 8.7.

b) Multi-temporal SAR (C-Band) from RADARSAT (Microwave imagery)

The RADARSAT satellite was launched on 4 November 1995 and has a Synthetic Aperture Radar (SAR) sensor on board. This sensor can operate in a variety of imaging modes to suit a range of applications. It provides useful information in the fields of agriculture, cartography, hydrology, forestry, oceanography, ice studies and coastal monitoring. A details description on various imaging modes of RADAR has been described and discussed in Literature review section of this Study.

In this study, two standard RADARSAT products namely 50-metre SAR Narrow Beam- 300 x300 km scene and 100-metre SAR Narrow Beam- 500 x 500 km scene is used. The 50-metre SAR is imported to .img format by using the RADARSAT (Vancouver CEOS) import application

40 RECONSTRUCTION OF 2003 DAYA RIVER FLOOD, USING MULTI-RESOLUTION AND MULTI-TEMPORAL SATELLITE IMAGERY

in ERDAS imagine both in DN value and sigma nought (sigma0) which is expressed in dB (commonly known as backscattering coefficient). The 100-metre SAR is obtained only in DN value.

Basically, the single radar image is usually displayed as a grey scale image and the intensity of each pixel represents the proportion of microwave backscattered from that area on the ground which depends on a variety of factors: types, sizes, shapes and orientations of the scatterers in the target area; moisture content of the target area; frequency and polarization of the radar pulses; as well as the incident angles of the radar beam. The pixel intensity values are often converted to a physical quantity called the backscattering coefficient or normalized radar cross-section measured in decibel (dB) units with values ranging from +5 dB for very bright objects to -40 dB for very dark surfaces (Cunjian et al., 1999).

The conversion from a pixel value to a backscatter coefficient is given by- Theoretically, radar backscatter co-efficient i.e. ° is derived using; 20log (DN) - 68.5 for JER-1 SAR and 10 log (DN2/A) + 10 LOG sin I for Radarsat SAR; where A = scaling gain (5695770.5); I=Incident angel (20.2o); DN = digital number/pixel value recorded from images. (Takashi Kurosu, 2001); (Hashim and Kadir, 1999) c) ASTER (Advanced Space Borne Thermal Emission and Reflection Radiometer) imagery from TERRA Satellite system.

ASTER is provided by the Japanese Ministry of International Trade and Industry (MITI). The contractors developing the major instrument subsystems are NEC, MELCO, Fujitsu, and Hitachi. It provides high-resolution images of the land surface, water, ice, and clouds using three separate sensor subsystems covering 14 multi-spectral bands from visible to thermal infrared. The significant resolution scales are 15m, 30m, and 90m in the visible, short-wave IR, and thermal fR, respectively. The VNIR subsystem operates in three spectral bands at visible and near-IR wavelengths, with a resolution of 15 m. It consists of two telescopes--one nadir-looking with a three-spectral-band detector, and the other backward-looking with a single-band detector. The backward-looking telescope provides a second view of the target area in Band 3 for stereo observations. The SWIR subsystem operates in six spectral bands in the near-IR region through a single, nadir-pointing telescope that provides 30 m resolution. The TIR subsystem operates in five bands in the thermal infrared region using a single, fixed-position, nadir-looking telescope with a resolution of 90 m. Unlike the other instrument subsystems, it has a "whiskbroom" scanning mirror. (http://asterweb.jpl.nasa.gov/, Assessed on: 12-09-2005). In this study, an ASTER image of 21st September 2003 is downloaded from the EOS Data gateway,(http://www.modiscluster.org/data/)and(http://edcimswww.cr.usgs.gov/pub/imswelcome/ ).It’s used for the visual interpretation of the flooded/inundation extent. The ASTER scene obtained covers only a small portion of the study area. Only the overlapping area’s with that of RADARSAT is used for extraction of inundation extent. In addition, it’s having a cloud cover of about 70%, which again creates problem in visually interpreting and delineating the land water boundary.

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4.1.2. Data from field/ other source

Various ancillary data are collected during the Field visit to the study area. A Map has been prepared where extracted Flood inundation extent of September 4th from Visual interpretation is overlay with LISS-III pre-flood image, (Scale: 1:50,000), which was then used for Field work for locating and verifying various features present in satellite imagery. Moreover, it was used as a waypoint to collect various GPS point of the study area. Data which are collected from various sources during Field visit pertaining to the current study are listed below:

Distribution of Flood Discharge in Lower Mahanadi System. (Source: Flood cell, Bhubaneshwor) Flood Water Level from 3 Gauge station- Kanti (85°46.15 E, 20° 08.10 N); Madhipur (85°48.1 E, 20°7.3N); Kanas (85°38.45 E, 20°06.45N) measured during 30 Aug. – 24 Sep., 2003.[Refer Figure: 2-3 (b).]

Historical peak flood (1964 – 2003) in Mahanadi River System at Naraj Railway Bridge. (Source: Flood cell, Orissa)

Daily Flood Reports during Aug. – Sep., 2003. (Source: Flood cell, Bhubaneshwor)

Geomorphology map of the region (ORSAC, Bhubaneshwor)

GPS point/location in & around Daya River system collected during field.

Maps showing Canal, Drains & Poor drainage areas between Daya & Bhargavi Rivers, under Mahanadi Delta Command Area. (Source: Department of Water Resources, Govt. of Orissa)

A schematic diagram of Daya River system prepared by Puri Irrigation Division, Puri is as shown in Figure: 4-4, which help in understanding the drainage pattern i.e. inflow and outflow of the Daya River at the lower catchments part near the Chilka lagoon.

42 RECONSTRUCTION OF 2003 DAYA RIVER FLOOD, USING MULTI-RESOLUTION AND MULTI-TEMPORAL SATELLITE IMAGERY

N

MALAGUNI SALIA KANSARI KUSUMI

DAYA

INFLOW

LUNA

Chilka Lake RATNACHIRA

BHARGAVI

OUTFLOW

BAY OF BENGAL

Figure 4-4 Schematic diagram of Daya River system (inflow to Chilka Lake)

A standard flood discharge level classification made by Flood Cell, Bhubaneshwar is collected which is used for analysis the dynamic of Daya River thereby improving the reconstruction of Flood event 2003.

The ranges of instantaneous discharge corresponding to various magnitudes of Flood (Gauging site: Naraj Bridge) are as shown in Table: 4-3 below:

Table 4-3 Standard Flood Discharge Level Classification

Discharge in Cusecs/Cumecs (Q) Magnitude of Probable Flood Q > 12,00,250/33,979 HIGH Q = 10,00,000 to 12,00,000 / MEDIUM 28,310 to 33,972 Q = 800000 to 10,00,000 / LOW 22,648 to 28,310 Q < 8,00,000/22,648 NO FLOOD

Source: Flood cell, Bhubaneshwor

To understand the distribution of Flood discharge in the lower Mahanadi River, where the main gauging site is at Naraj Railway Bridge, a schematic flood distribution network of the region is prepared from the information’s obtained /collected from Flood cell, Bhubaneshwor. The schematic network is as shown in Figure: 4-5.

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AT NARAJ 15, 80,000 Cusecs/44734 Cumecs (Severe Flooding situation)

58% 42 %

KATHAJODI AT NARAJ 25945.7 Cumecs MAHANADI AFTER NARAJ 18788.2 Cumecs 16.2 % 41.8% 35% 7% KUAKHAI RLY.BRIDGE KATHAJODI N.H.Bdg., 18698.8 MAHANADI N.H.-5 Rd.Bridge BIRUPA N.H.-5 Rd.Bridge 7246.9 Cumecs Cumecs 15656.9 Cumecs 3131.3 Cumecs

12.64% 3.65% KUAKHAI AT Rd. Bridge MANCHESWAR SPILL 1632.7 Cumecs 5654.3 Cumecs

KUSHABHADRA 1843.04 DAYA 1968.2 BHARGAVI 1843.04 4.4% Cumecs Cumecs Cumecs 4.12% 4.12% 70% Inflow to Chilika 30% Bay of Bengal

Source: Flood Cell, Government of Orissa, Bhubaneshwar

Figure 4-5 Distribution of Flood Discharge in Lower Mahanadi System The Cartographic Map showing canal drains and poor drainage area – Daya Bhargavi Doab (Doab VII) under Mahanadi Delta Command Area, helps to identify the permanent wetland, swampy, back swamp area present in the area under investigation i.e. study area. It’s shown in Figure:4-6 (Source: Puri Irrigation Dept.,Orissa) which referred to the various canal networks, Poor drainage area, escapes and sluices, river with embankment and the gauging stations.

Figure 4-6 Map showing canal drains and poor drainage area Daily flood report which is prepared by Flood cell, Bhubaneshwor is collected for the flood period i.e. August-September 2003, for the analysis purpose of the Flood event, 2003. Moreover, Historical peak

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Flood data i.e. from 1964 to 2003, for the Mahanadi River system at Naraj Railway Bridge is obtained from Flood cell, for Flood frequency analysis i.e. determination of Recurrence interval and the exceedence probability.

4.1.3. Digital elevation model a) DEM Extraction from Field Maps (1 m-contour interval)

Map digitization is still being used widely as means for creating DEMs for certain applications. However, the study area being a costal region of India, which falls under restricted zone, the toposheet of the area is not available for generating DEM.

Hence, a field map of the study area (acquired during field work) i.e. Cartographic Map showing canal drains and poor drainage area – Daya Bhargavi Doab (Doab VII) under Mahanadi Delta Command Area, which helps to identify the permanent wetland, swampy, back swamp area present in the area under investigation is used for extracting the contour lines which is having a interval of 1 metre by onscreen digitization. Then, the derived contour map is used to generate a DEM of the study area. The cell size of output DEM is kept at 15 meter pixel spacing. The generated TIN and its corresponding DEM model are as shown in Figure: 4-7 and 4-8 respectively below:

Figure 4-7 TIN Model Figure 4-8 DEM generated from Field Map

b) DEM Extraction from ASTER:

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The ASTER Digital Elevation Model is a product that is generated from a pair of ASTER Level 1A images. This Level 1A input includes bands 3N (nadir) and 3B (aft-viewing) from the Visible & Near Infra-Red telescope's along-track stereo data that is acquired in the spectral range of 0.78 to 0.86 microns, having a vertical accuracy of 7m and horizontal resolution of 30m (Kamp et al., 2003). An attempt has been made in PCI Geometica v 9.1.7 to generate a relative ASTER DEM by using the AST_LIB_00309082001050207: Aster VNIR Band 3B (0.8040), Aster VNIR Band (0.8070). Initially an epipolar image is created using the two input bands, and then DEM is extracted automatically by using this epipolar image after Bundle adjustment. Then, the resulted DEM is edited manually to fill hole and sink i.e. failure surface. But the result obtained is not satisfactory to be used as an input parameter for GIS approach, as the accuracy of the generated DEM is very low. This is due to the fact that enough GCP with sufficient elevation height is not available for model calculation (only tie points are generated in this process). The basic step involved in Geometica and the resulted DEM is illustrated and shown in Figure: 4-9 and 4-10 respectively as below:

Figure 4-9 Steps involved in automatic generation of DEM using ASTER (VNIR) in Geometica v.9.1.7

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Figure 4-10 Automatic Extracted DEM (many failure surface can seen on the image which is represented by dark areas) As a last attempt relative ASTER DEM available on the pubic domain of EOS Data gateway was downloaded from the internet. Three scenes were downloaded and mosaic it to extract out the region of interest. Basically, in this too, the DEM obtained was not accurate when comparing the elevation value of the overlapping portion in three scenes, as the different scene gives different elevation value for same location. The mosaic DEM indicating the study area is as shown below in Figure: 4-11.

Figure 4-11 Mosaic DEM as obtained from EOS Data gate way (http://edcimswww.cr.usgs.gov/pub/imswelcome/)

47 RECONSTRUCTION OF 2003 DAYA RIVER FLOOD, USING MULTI-RESOLUTION AND MULTI-TEMPORAL SATELLITE IMAGERY

4.2. Methods

Broadly two types of approach are being adopted and implemented here for this study. They are: i) Remote sensing Approach ii) GIS Approach. In addition, Historical data analysis is also made and integrated by Flood frequency analysis of maximum flood discharge of 40 years and that of geomorphic unit analysis is also attempted to understand the dynamics of the Daya system.

In Remote sensing approach, various techniques of extraction of flooded extent have been performed to multi-temporal RADARSAT imagery and optical datasets. This will help us to analyse the inundation pattern extracted from various datasets by visually interpreted i.e. visual interpretation and digital techniques using various classification techniques/algorithms such as supervised, unsupervised, Thresholding/density slicing, Textural analysis based classification and PC-based classification (Principal component analysis based classification). And a comparatively analysis of inundation extent extracted is made and presented here in this study. Moreover, using multi-temporal dataset like the one which is used in this present study i.e. RADARSAT would also help us to know the expansion of flooded areas. In short, the digital techniques (automatic classification) adopted here in Remote sensing approach would help to find a quick, accurate and operational method for flood mapping.

In GIS based approach, a methodology is developed to estimate the flooded area at the peak using flooded inundation extent observed after the flood event seen on the multi-temporal RADARSAT imagery of 4th ,11th ,13th and 20th September 2003 and ancillary information extracted from 1m contour field map derived DEM. The basic assumption taken here is that water has to flow out from the two embankment breach identified in the satellite imagery (as shown in Figure: 4-12) along the main river channel and it will spread up to reach at least the areas recognized as maximum flooded extent in the multi-temporal RADASAT imagery. Then, a least accumulative cost-distance surface matrix is generated, which in turn fills up the flooded place by tracing backwards from the source i.e. breach locations towards still remaining water observed in RADARSAT imagery. Generally, the least accumulative cost-distance matrix is modelled as a continuous, cumulative function from the main river stream up to the limits of the study area using a recursive procedure common to many GIS environments (P.A.Brivio et al., 2002). In this study, the matrix is modelled from the point/opening of embankment breach which is on the right bank of Daya River. An automatic procedure implemented in ARC/INFO and ARCGIS 9.0 for generating the matrix is used in this study.

Daya river Floodplain

Escape channel

Daya river & the Escape channel Madhi pu r e s ca pe & K a nti e s ca pe a s s e e n Figure 4-12 Two embankment breach as observed in IRS-PAN (5.8m) on Sept. 08 2003 in IRS-PAN(5.8 m)

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4.2.1. General Overall Methodology

The whole methodology is structured into three main broad approaches. A schematic outline/flow chart is as shown in Figure: 4-13 below. In Historical approach, analysis of historical data pertaining to flood events are made like flood frequency analysis, geomorphological analysis. This in turn will help us to understanding the dynamics of the Daya River system associated with flooding phenomenon. In addition, the feasibility and usefulness of integrated RS and GIS approach is being evaluated for extraction of flood information.

Historical GIS Satellite Data (Multi-sensor & Multi- Ancillary Data Approach- resolution approach) data collection of DEM Different the area sources/Digital topography Pre-Flood Data Post-Flood Ancillary data Data i.e. topographic, Cartographic, Analysis of Satellite Geomorpho- Data- Visual & Digital Analysis logical data interpretation Generate least accumulative cost- distance matrix Flood inundation Map

Temporal Flood Flood inundation at Inundation map their maximum peak discharge Derived Flood Occurrence phenomenon/dynamics & pattern in and around Daya River system

Figure 4-13 General Methodology (combination of Historical, RS, GIS Approach)

4.2.1.1. Remote sensing approach

The Schematic outline of the Remote sensing Approach is shown in Figure 4-14: In Remote sensing approach, various Digital classification technique are adopted for mapping the inundation extent and analysing the variation in extent in various dataset w.r.t. their spatial and temporal characteristics.

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IRS-PAN(5.8m) Remote sensing Approach Satellite Datasets LISS-III (23.5m) ASTER (15m) Supervised classification RADARSAT (50m, 100 m) (pre-flood imagery)

•Visual Interpretation Image with pre-flood water extent •Thresholding /Density slicing •Supervised – MLC and Minimum- Land cover map • distance to Mean •Unsupervised Classification- Iso data **Comparison of flood inundation •clustering •Textural analysis based classification •PCA analysis based classification Overlay

Actual submerged extent Overlay & analysis

DEM Submerged Land use/cover map Inundation extent based on elevation (DEM)

Figure 4-14 A schematic outline of Remote sensing approach

4.2.1.2. GIS based approach

The GIS based methodology is based on generation of a least accumulative cost-distance surface matrix using a DEM (Digital Elevation Model) which in turn fills up the flooded place by tracing backwards from the source i.e. breach locations towards still remaining water observed in RADARSAT imagery. A brief schematic outline of this approach is as shown in Figure: 4-15 below.

Datasets: SRTM DEM, Aster GIS Approach DEM,Field contour Map (1 m), #

Digital Elevation Model Ancillary data- topographic data, surface characteristic data e.g. roughness, land use, vegetation cover

Slope map Aspect map Least accumulative Arc info Raster based analysis/ cost-distance matrix ARC GIS 9.0 (Arc Map) Flooded area map, integrated Radarsat imagery

# Stereo data from Maximum Flood CARTOSAT (2.5 m) inundation extent

Figure 4-15 Schematic outline of GIS approach

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4.2.2. Software used:

Basically, there are various image processing and GIS capabilities software for processing and analysis of remotely sensed datasets. Here, ERDAS Imagine 8.7 is used for vector analysis i.e. onscreen digitization of the satellite imagery, Principal component analysis and for digital image classification purpose. The Radar based module of ENVI 4.1.is used for generating the co-occurrence measures for Textural based analysis. Spatial analyst module of ARC GIS 9.0 is basically used for generating classified map and analysis of the various classification techniques such as Thresholding, unsupervised classification and reclassifying of the textural images i.e. for determination the areal extent of inundation in different datasets. Moreover, the Distance tools of the ARC Toolbox (spatial analyst tools) and that of ARC INFO Spatial Raster based is used for processing and analyzing the least accumulation cost distance matrix. ILWIS 3.2 is used for interpreting and analyzing the geomorphic units inundated due to flooding and for preparation of flooded geomorphic units map. Microsoft excel 2003 is employed for generating various database for historical flooding analysis i.e. flood frequency analysis and plotting of various graphs for interpretation and analysis.

4.2.3. Application of Different techniques for Flood inundation extent mapping

There are various techniques that can be used for estimating the inundation extent due to flooding. In this Remote sensing approach, some of the techniques adopted for inundation extent extraction i.e. land-water boundary delineation are described in the following sub-heading with details working procedure adopted.

4.2.3.1. Visual Interpretation

For a Satellite data analysis, Visual interpretation is considered to be one of the most effective and reliable technique for extracting the inundation extent thereby resulting an accurate flood boundary. But, one of the drawbacks is that of low efficiency in interactively manual interpretation that limits quick, dynamics and accurate acquirement of flood information at the time of emergency and mitigation preparedness.

Visual interpretation of the multi-temporal RADARSAT of 4th, 11th, 13th and 20th September 2003 (dB, 50 metre and DN, 100 metre) was carried out by onscreen digitization in ERDAS Imagine 8.7. In case of 50 metre DN image, when visually comparing with 50 metre multi-temporal RADARSAT dB image the land –water boundary seems to overlay exactly over one another in the imagery. Moreover, the swiping option in EARDAS Imagine is employed to see there is any difference in land-boundary extent i.e. inundation extent. Hence, visual interpretation of 50 metre DN image is considered to be same and equal as that of 50 metre dB image. Pre-flood pan sharpened LISS-III (23.5 metre) imagery of January 16th 2003 and a field map of 1:50,000 are used as reference imagery while delineating the land-water boundary from the multi-temporal and multi-resolution RADARSAT imagery. All the optical dataset are also visually interpreted by onscreen digitization. In case of LISS-III, panchromatic and pan sharpened LISS-III of September 8th 2003 due to presence of cloud over the study area, it

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hinder in proper delineation of land-water boundary. In case of ASTER image of September 21st 2003, the whole study area extent is not contained in the scene. Hence, only the overlapping portion which is about 40% of the study area is used for visual interpretation purpose. Moreover, there is contamination of the ASTER imagery with cloud cover which is about 70 % of the whole scene. Once, digitization of the datasets is completed, the vector layer is clean and build and is converted to shape file and finally to grid (Raster format). The permanent water bodies i.e. river network is delineated from pre-flood Pan (5.8 metre) imagery of January 16th 2003, and it’s used as a pre-flood water extent image. Then its overlay with the digitized two classes’ grid image i.e. flooded and non- flooded class, thereby resulting into three classes i.e. flooded, non-flooded and permanent water bodies, which gives the actual submerged and inundation extent due to flooding.

4.2.3.2. Thresholding/ Density slicing

Thresholding/Density slicing simply means dividing the histogram into two or more parts. To each sliced spectral range, an identity or class name is assigned accordingly. Generally, slicing is considered to be accurate when the corresponding histogram of the image shows a bimodal distribution, but on the contrary histogram bimodality itself does not guarantee correct threshold segmentation.

There is a clear distinction between water and surrounding objects on radar image. Thresholding based classification are made to multi-resolution and multi-temporal RADARSAT imagery of 4th, 11th, 13th and 20th September 2003. An example of this technique is shown in figure24 where Thresholding is made on September 4th RADARSAT imagery. Reclassifying/ slicing of the histogram of each of the imagery are done in ARC GIS 9.0 platform. There is various method of classifying/slicing the histogram which is available in “Reclassify” option in Arc Map. They are namely- Manual, defined interval, equal interval, quantile, natural breaks (jenk), standard deviation method. Here, Manual method is adopted so that a correct and precise threshold range can be achieved by interactively slicing the histogram according to user need.(Refer: Figure: 4-16)

Figure 4-16 Thresholding of September 4th RADARSAT imagery (dB value, 50m) Initially, the range of pixel value is studied and interactively finds out the difference of radar dB and DN value between flood water and non-inundated areas. This in turn helps in land-flood

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discrimination whereby the histogram is reclassify into two main broad classes i.e. flooded and non- flooded. Exceptionally for some imagery due to difficulty in assigning the correct threshold, it has been sliced into three classes i.e. deep flooded, Shallow flooded and Non-Flooded. Then deep flooded and shallow flooded were merged together into one class- flooded, for getting the areal extent of total flooded area in the study region for comparison purpose. The resulted image is converted to grid i.e. flood mask images, in which the flooded area and the non- flooded area were evaluated 1 and 2 respectively. The permanent water body in normal years is extracted i.e. preparation of water mask image from pre-flood Pan (5.8 metre) imagery of January 16th 2003 is done, and it’s converted to grid having two classes’ i.e. permanent water body and other classes having 0 and 1 grid value. Then, with the help of Raster calculator in Arc Map, the final map showing the real and actual submerged extent is obtained for all the imageries having three classes i.e. flooded, non-flooded and permanent water bodies. Afterward, the resulted classified images are used for deriving the respective areal extent i.e. area of submergence due to flooding at each date is calculated.

4.2.3.3. Unsupervised classification

Generally, Clustering algorithm is used for unsupervised classification. Clustering implies a grouping of pixels in multi-spectral space. Pixels belonging to a particular cluster are therefore considered to be spectrally similar. A similarity measures is devised to quantify the relationships of spectral similarity. The most frequent used similarity metrics is that of Euclidean distance and interpoint distance. In this technique, an image is segmented into unknown classes. It’s the task of the interpreter to interpret and label those classes, thereby resulting into a classified image.

One of the advantages in unsupervised classification is that no prior knowledge of the region is required extensively. The opportunity of human errors is minimized. Unique classes are recognized as distinct units. Moreover, if the analyst is interested to distinguish classes spectrally, then, unsupervised approach should be adopted as this approach has potential advantage of revealing discernable classes unknown from previous work.

The Iso-data clustering algorithm which is implemented and readily available in ERDAS Imagine 8.7 is used for classification of the multi-resolution and multi-temporal RADARSAT imagery. Basically, it is based upon estimating some reasonable assignment of the pixel vectors into candidate clusters and then moving them from one cluster to another in such a way that the SSE (sum of squared error) measure of the preceding section is reduced. Initially, 20 classes are made with Iso data clustering algorithm in ERDAS Imagine 8.7 and the number of iteration is made to 12 times under convergence threshold of 0.950 (default value). Once output cluster layer and each corresponding signature file are generated for all the dataset, they are imported to Arc Map for reclassifying and recoding into desired classes. All the 20 classes are recoded to two classes assigning a grid value of 1 and 2 which correspond to flooded and non-flooded classes respectively. Then, the permanent water bodies is extracted out by overlying with the output grid file thereby finally results into a thematic classified map having three classes i.e. Flooded, Non-flooded and permanent water bodies.

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4.2.3.4. Supervised classification

In supervised classification, the number of the separable patterns that exist in the image area is known or at least can be estimated. Ideal patterns can be formed by estimating and comparing the statistical properties i.e. mean and standard deviation of the values of each of these separable patterns. This is done by selecting training samples that contains these separable patterns i.e. real and familiar classes that can be recognised in the scene. Most importantly, selection of an appropriate classification algorithm- whether parametric or non-parametric, helps us to assign an unknown pixel to one of numbers of classes. In short, it is generally preferred for classifying a remotely sensed data, where the terrain is smooth and the possibility of locating a training site that can be achieved with higher accuracy. Moreover, in simple words, it can be put as a process of sampling of known identity to classify pixels of unknown identity, where a sample of known identity lies on training sites.

There are different algorithms of supervised classification that the remote sensing software support such as Maximum Likelihood, Minimum Distance, Parallelopiped and Mahalonobis distance techniques. Here, in this context, two parametric algorithms are used namely Maximum Likelihood and Minimum Distance to mean which is implemented and readily available in ERDAS Imagine 8.7.

Maximum likelihood (Bayesian) and minimum-distance-to-mean algorithms are pixel specific classification algorithms for classifying the spectral content of digital images(JamesR.Carr, 1999). Maximum likelihood classifier is based on the probability that a pixel belongs to a particular class. The basic equation assumes that these probabilities are equal for all classes, and that the input bands have normal distributions. The effectiveness of maximum likelihood classifier depends upon the accurate estimation of the mean vector and the covariance matrix for each spectral class. This in turn is dependent upon having a sufficient number of training pixels for each of classes. On the other hand, Minimum Distance classifier (also called spectral distance) calculates the spectral distance between the measurement vector for the candidate pixel and the mean vector for each signature. This classifier uses the training sample for determination of class means and then classification is done by placing a pixel in the class of the nearest mean.

Supervised classification is divided into selection of training sites, evaluating signatures, applying classification algorithms to each of the imagery. In addition, the total areal extent of inundation on each date is determined from each of the classified thematic map/images. Initially selection of training site/sample is done by generating a spectral surface profile. Then, statistical evaluation of training samples is made by generating a separability matrix and contingency matrix, where it helps in refining the training samples. The training samples having small separabilities are merged together to form one signature. Contingency matrix i.e. error matrix allows to see how many pixels in each sample are assigned to each class.

Finally, a classified thematic map for all the imageries is produced having three main classes i.e. deep flooded, shallow flooded, non-flooded. For analysis purpose, Areal extent of each class is also generated in GIS environment where the classified map is converted into a four class grid map after overlaying the permanent water bodies image thereby resulting to deep, shallow, non-flooded and permanent water bodies having class value of 1,2,3 and 4 respectively. Deep and shallow flooded are taken into categories of inundated area’s for comparison and analysis purpose. Detailed analysis and results are discussed in succeeding chapters.

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4.2.3.5. Principal component analysis

Principal component analysis are generally applied to reduce the dimensionality of the data set i.e. it’s a procedure for transforming the set of correlated variables into a new sets of uncorrelated variables. Generally, those features that are hidden in the data are brought out by these techniques and in turn help in increasing the efficiency and accuracy of image classification and interpretations both in visual and digital approach of classification. Theory behind Principal component dictates that the information content of the PCs is compressed into the PCs in order of descending significance, with the lower- numbered PCs containing the primary information content, and the higher-numbered PCs containing other information and noise. In PC analysis there is transformation i.e. rotation of the original axes to a new orientations that are orthogonal to each other and hence, there is no correlation between variables. In other words, it can be described as the translation and rotation of the original coordinate system into a new coordinate system that better reflects the principal modes of variability in the data set being analyzed (RAMMB, 2005).

In Remote sensing context, because of its ability to simplify multi-spectral data sets, Principal component analysis which is also known as Eigen vector/eigen value analysis has been used extensively for the analysis of high-spatial-resolution environmental (land and ocean) remote-sensing imagery (B.R.Corner et al., 2003). However, the technique is also being utilized for analyze various multi-sensor and multi-temporal satellite imagery, for application in various thematic contexts. Regardless of the intended application where PCA is to be adopted, it determines which part of the multi spectral signal is common to all the images (spectral bands) and separates that information from other image information that is sensed only by image differences or multiple image combinations. Generally original satellite images may (and often do) contain redundant information, so the principal component images derived contain the independent signal separated out of the original images. This in turn allows the image analyst/interpretator to see the independent components of multi spectral imagery, thereby increasing the accuracy of the interpretation and classifications.

Here, initially the Multi-Date RADARSAT imagery of Sep.04, 11, 13 and 20th 2003, both dB and DN images having spatial resolution 50 metre and 100 metre is used to generate a FCC of the imagery and in turn is subjected to Principal component transformation in ERDAS Imagine 8.7. When the four multi-date Radar imagery are treated as a separate variables and subject to transformation, the ordering of the principal component is influenced both by the spatial distribution of the various surface materials and image statistics. A detailed analysis is made in subsequent chapters where the inundated extent extracted out by using this technique is compared with that of inundation extent result obtained using other techniques. Eigen vector matrix is also generated thereby examination of eigenvector loading enables one to decide which component images will contains information’s directly related to spectral signatures. Percentages of Eigen values generally indicate the percentage of variance explained in each vector. Moreover, Color combination of PCI bands for better separation of inundation extent i.e. land water boundary separation is explored in this study.

4.2.3.6. Textural analysis based classification

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Various definition of “Texture” is formulated by different people depending upon the particular application and there is no generally agreed upon definition. Some are perceptually motivated, and others are driven completely by the application in which the definition will be used. The perception of Texture is believed to play an important role in the human visual system for recognition and interpretation. Texture classification, Texture segmentation and Texture synthesis are three broad main areas of Image Textural analysis.(Mihran Tuceryan, 1998)

Basically, Image texture is defined as a function of the spatial variation in pixel intensities (gray values) and it is useful in a variety of applications and has been a subject of intense study by many researchers around the world. Recognition of various features using texture properties is one of the immediate applications of textural analysis. Texture as such is one of the important visual cues in identifying homogeneous region in imagery. This is known as “Texture based classification” and its goal is to produce a classification map of the input image where each uniform textured region is identified with the texture class it belong to. Textural based classification is a technique of neighbourhood algorithm based classification i.e. a neighbourhood classification scheme is needed when attempting the classification of image texture (JamesR.Carr, 1999) In Textural analysis, one of the most important tasks is to extract texture features which completely embody information about the spatial distribution of gray level in the image. There are two major approaches- statistical approach and structural approach.

Here, in this study, the statistical approach is adopted where it relates to statistical parameters which characterize the stochastic properties of spatial distribution of neighbouring gray level of image. Generally, statistical approach is based upon Grey-level co-occurrence matrices (GLCM). Co- occurrence matrices count how often pairs of gray level pixels that are separated by certain distance and lie along a certain direction, occur in digital image. It is essentially a two dimensional histogram of the number of times that pairs of intensities values occur in a given spatial relationship. It forms a summary of sub-patterns that could be formed by grey level pairs and the frequency with which they occur (M.Haralick et al., 1973)

A) Extraction of Textural Features:

An automatic inbuilt algorithm/procedure implemented for Texture filters in RADAR module in ENVI 4.1 is used for generating the co-occurrence matrices for the available multi-temporal and multi- resolution RADARSAT imagery of 4th, 11th, 13th and 20th September 2003. Several textural measures can be computed from the GLCM in order to describe specific textural characteristics of the imageries. A set of features/ textural measures defined by Haralick are implemented in the ENVI 4.1. and it can be directly computed to generate corresponding textural measure imageries for the given set of input images. Over each of the four RADARSAT images, a series of co-occurrence textural features is computed using a sliding window (kernel window) of fixed 15 x 15. The displacement considered for building the co-occurrence matrix was 1 pixel in the horizontal row/direction i.e. a co-occurrence shift of 1 is used. This choice was made for the following reasons: i) to catch the finest details and variation between each feature present in the imagery, the kernel window is kept at 15x15 i.e. gap size; when computing textures ii) and since no particular anisotropy is visible in the images, it is recommended to adopt the default co-occurrence shift of 1 i.e. X and Y shift.

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With the above parameters a total of 7 textural features are computed for analysis, namely: Variance, Homogeneity, Contrast, Dissimilarity, Entropy, Second Moment and Correlation.The Co-occurrence measures processing window available in ENVI 4.1 and one of the resulting texture images of September 11 2003, dB value is shown in Figure 4-17 below.

Figure 4-17(Left) Co-occurrence texture measures computation window and (right) one of the resulting texture image

B) Thresholding of Textural features:

Once the Textural measures images are obtain a classified textural based image is generated by adopting simple Thresholding/Density slicing technique i.e. threshold-based image classification is made in ARC Map. The textural image is classified into three categories by selecting a suitable range of threshold within its corresponding range of dB and DN values of each image. Then, comparison of the threshold range is made with respect to the percentage deviation of the inundation extent area obtained at each measure image from that of inundation extent obtained by visual interpretation. Detail analysis and finding are discussed in later part in the light of percentage variation in inundation extent in various textural measures.

4.2.4. GIS Methodology

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4.2.4.1. Generation of least accumulative cost-distance surface matrix and obtaining the Maximum inundation extent corresponding to actual peak flooding

As discussed in Section 4.2 above about the GIS Methodology, the main step is the generation of a least cost-distance surface matrix, using an accurate DEM that represent the actual topography of the region, which in turn is then used to integrate with the inundation map derived from RADARSAT to get the inundation extent map that correspond with the peak flood discharge. This in turn would help us to overcome the constraint of temporal resolution in the application of satellite imagery in flood inundation mapping. Moreover, the time delay between the actual flood peak and the satellite overpass can be overcome by implementing this methodology. A temporal relationships between flood event, flooded area and RADARSAT image acquisition shows the difference in flooded area at the peak (Tp) and the flooded area after the event i.e. during recession period i.e.( Tp +∆T),which is captured in RADARSAT imagery. Basically, ∆T is the time difference/delay between the peak flood phase and the satellite observation. It’s explained interactively in the form of a diagram as shown in figure 26 below.

Heavy Rainfall ea Flood Peak RADARSAT IMAGE d Ar

e Acquisition d o o l F

Recession Period

To Tp To +∆T Flooding Recession

Source: (P.A.Brivio et al., 2002)

Figure 4-18 Temporal Relationship between flooded area, flood event and Radarsat Observation.

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Using the acquired DEM of the study area, the “least accumulative cost-distance” matrix is derived. This derived grid/raster image have its cell value characterized by a value equal to the least accumulative cost that water has to spent to get to the cell departing from main stream/channel of the river near the breach and escape channel. Here, cost implies to the work necessary for the water to overcome resistance formed by natural and anthropic features such as topography, surface roughness, and land use type i.e. all factors that affect water flow over the terrain/surface.

An automated procedure implemented in ARC/INFO Grid based and ARC GIS 9.0 is used for this study. The main steps involved in generation of least accumulative cost-distance matrix are discussed as below: i) Initially, the cost-distance i.e. cost required for water to move from identified source cell to each of the neighbouring cells, i.e. cost-distance between two any adjacent cells A and B, is calculated by using the formula given

Where cost A and cost B are value associated with each cell A and B, distA_B is the distance between cells A and B. VF and HF are horizontal factors accounting for topography and surface topology respectively. Basically 2 main input parameters is required to generate the cost distance and each of them (e.g. between any two neighbouring cells A and B) is described as follows-

The distance between the two cells i.e. distA_B is calculated by Pythagorean Theorem, using a representation in which the centres of two adjacent cells are connected by links. The Cost value i.e. cost A and cost B associated with each cell is a weight factor proportional to the resistance incurred by water when flowing over the cell. It is per-unit distance measure and in this it will be represented by the elevation of the region i.e. DEM. Vertical Factor (VF) and Horizontal Factor (HF) is the two factors for generating a least accumulative cost-distance matrix. The cost necessary to account/overcome for height difference between two cells is taken care by Vertical factor. It’s computed on basis of Slope map derived from DEM and values are assigned according to ARC GIS implementation procedure of this tool. On the other hand, the Horizontal factor accounts for any friction encountered by water flowing over the surface and is linked to the surface characteristics like roughness, landuse/landcover pattern etc. As such this factor is very complex to model ((P.A.Brivio et al., 2002), so the surface was assumed to be constant over the entire area and a default value of one is set everywhere within the extent of study area.. Details description can be referred to ARCGIS Desktop Help section where all the values according to the slope are given. In addition, in ARCGIS environment, as compared to earlier ARC INFO workstation, this technique is more simplified and become more users friendly.

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From RS Approach

Source cell, River bed From GIS Approach

1. detected flooded areas from satellite data (RADARSAT) 2. Generate Least accumulative cost distance matrix from DEM of the study area (where the origin is river) 3. a cell in the study area is flooded if its cost is lower than the cost of the cell detected from the satellite data (RADARSAT).

Source: (P.A.Brivio et al., 2002)

Figure 4-19A Schematic diagram showing integration of RS and GIS Approach using least accumulative cost distance matrix.

4.2.4.2. Execution the technique in ARCGIS 9.0 Platform

The cost-distance function i.e. function which calculates for each cell the least accumulative cost distance to a nearest source over a cost surface, which under the Distance tools in Spatial analyst was used to generate the least accumulation cost-distance matrix. Two main input parameters i.e. the source grid and the cost grid was generated initially, before executing the function (Lindermann and Page, 2004).

In this application for generating the source grid, the source points are taken as the embankment breach locations in the main river channel where flood water flow out and inundate the surrounding areas. Initially, a blank raster grid is generated and the 6 source points are located nearby the embankment breach as can be seen from the IRS- PAN image of September 8th 2003. The final source grid contains six identified source points having a Raster value of 1 which are to be processed as source cells for the function. All non-source cells are assigned NODATA on the source grid.

For generating the Cost Grid, two parameter are considered i.e. Elevation of the area and the Landuse/ landcover distribution of the region.

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Both the dataset are rescaled to a common weight factor and their percentage influence to the flow of water is determined. For instance, those cells correspond to higher elevation and under Forest class, is given a higher scale than those cell having low elevation under same class of Landuse type In short, the cost grid was taken as the elevation data i.e. DEM generated using the field map having contour interval of 1 meter and that of Landuse map where each classes present is given a weighted value/scale according to the resistance incurred by each features to the flow of water. Finally, cost grid is obtained after adding the reclassified datasets together. Here elevation factors is considered to be more important than Landuse/landcover of the region. For instance, avoiding hgher elevation/slopes may be twice as important as the landuse type, so elevation dataset is given an influence of 70 percent and the landuse dataset an influence of 30 percent (to make 100%).

Then, finally the function is executed to obtain the Cost distance raster, which identifies the least accumulation cost for each cell to return to the closest cell in the set of identified source cells. However, it is difficult to identify which source cells to return to. In addition, this function generates a Backlink raster, where it can be used to reconstruct the path to the source.

Then, the generated cost distance raster is tried to integrate with the flooded area map derived from the Remote sensing approach analysis by visual interpretation of 50 m dB image of 11th September 2003.

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5. Results and Discussion

5.1. Field Data Analysis

For any application where remote sensing techniques are adopted, a strong field and ancillary data is necessary to understanding and validate the approaches. Field data helps to understanding and provide a linkage with the real phenomenon happening thereby strengthening the result that are derived from Remote sensing and GIS technology. Here, in this study an important issues related to Historical data analysis and geomorphic unit analysis related to Daya Flooding event 2003 is given a special emphasis.

5.1.1. Historical Data Analysis

Documentary evidence, literatures and past studies on the specific area under consideration not only provides historical facts about a certain problem at a particular time but can also give us insight into various environmental themes. Using such records and data, it would help us to analyse different phenomenon under consideration in term of their temporal dimensions and extreme expression. Here, in this study records in the forms of literature about the past flooding event in Orissa is obtained during field work. Moreover, reference to past peak flood level and discharge for 40 years is analysed herewith to see whether this information will help us to form a “flood stage indicators” for the future trend. Moreover, prevailing meteorological information and discharge at various locations during 2003 flood is presented to analyse whether any extreme deviation from the normal is observed and to see the time-shift of the peak flooding day w.r.t. the satellite data acquisition days. All this analysis would help us to know whether the flood-producing mechanisms in the past have been same with that of the past or whether there is a deviation in the trend observed. So, properly and accurate reconstruction of any past flood events requires historical information which provide a complete picture to understanding the real perspective of that event.

5.1.1.1. Recurrence interval and exceedence probability analysis for 40 years Peak Flood discharge

Determination of flood flow for different recurrence interval is an important statistical analysis approach in flood related studies. The length of the period of record is also important for flood frequency analysis. Generally, for this approach to be adopted, a long enough i.e. longer time series stream flow/flood discharge records should be available to warrant the statistical analysis process.

63 RECONSTRUCTION OF 2003 DAYA RIVER FLOOD, USING MULTI-RESOLUTION AND MULTI-TEMPORAL SATELLITE IMAGERY

Moreover, it will be problem for river basins/ channel where flood water flow is appreciably altered by reservoir regulation, channel improvements or changes in land use pattern.

In this case, since sufficient hydrological data related to the Daya river system is not available, only analysis of recurrence interval and exceedence probability is made for the past 40 year maximum peak flood discharge which was observed at Naraj Railway Bridge Gauging site which is located upstream of the Daya river which is under investigation in this study.

The formula used to determine the mean recurrence interval is given by ---

If over n years, flood date is recorded and ranked in order of magnitude, then the mean recurrence interval, in years of m magnitude flood, can be taken as T= (n+1/m), where n = time-period and m = order of magnitude (rank order). Its exceedence probability is given by 1/T. In this study 40 years maximum peak discharge, the recurrence interval and peak discharge is calculated and is shown below:

Its seen from the past 40 years peak flood discharge data that the highest flood ever recorded within this 40 years time period occurred in 1982 with a peak flood discharge of 44750 Cumecs at a depth of 28.52 m. The Daya Flood event during 2003 is the third maximum flood discharge at a depth of 27.74 m. A graph representing the flood frequency vs. maximum peak discharge; for the past 40 years is as shown in Figure: 5-1

Flood frequency for 40 years Peak Flood discharge

) 45.00

ecs 40.00 35.00 cum

3 30.00

10 25.00

e ( 20.00 g 15.00

har 10.00 c

s 5.00 i

D 0.00 6 0 5 6 6 6 4 6 2 1 1 3 0 7 8 1 1 5 1 8 6 4 3 2 1 0 0 6 . .

5. 4. 3. 2. 2. 1. 1. 1. 1. 1. 1. 1. 41 13 Return Period

Figure 5-1 Flood frequency curve for past 40 years

64 RECONSTRUCTION OF 2003 DAYA RIVER FLOOD, USING MULTI-RESOLUTION AND MULTI-TEMPORAL SATELLITE IMAGERY

Relationship between Maximum Discharge and the exceedence probability (1/Tr)

50.00 45.00

ecs) 40.00 m

u 35.00 C

3 30.00 0

1 25.00 (

e 20.00 g

ar 15.00 10.00 sch i

D 5.00 0.00

7 7 4 2 9 6 8 6 3 0 8 02 0 .1 .2 .3 .3 .4 54 61 6 .7 .8 .9 .9 0. 0. 0 0 0 0 0 0. 0. 0. 0 0 0 0 Exceedence probability

Figure 5-2 Relationship between the discharge and exceedence probability (1/Tr)

It’s clearly seen from Figure: 5-1 that the highest flood event has a return period of 41 years and that of flood event having the same flooding magnitude like that of Daya has a return period of 14 years. Also, when there is a discharge above 25000 Cumecs, its exceedence probability range between 2% to 5%. A large magnitude flooding has a low exceedence probability when compared with event having less discharge level.(having a probability of 98 %). A graph showing the trend of flood gauge height and peak discharge with respect to its corresponding 40 year time-period is as shown in Figure 5-3 and Figure 5-4 below:

Trend in discharge level for past 40 years

50000.00

45000.00

40000.00

35000.00 cs

me 30000.00 Cu n 25000.00 e i g r a h

c 20000.00 s Di 15000.00

10000.00

5000.00

0.00

3 4 6 4 1 0 7 0 82 01 0 80 92 91 9 78 69 95 77 76 73 8 85 83 97 8 75 93 98 67 74 68 7 90 72 64 7 99 79 81 02 66 96 8 88 65 89 0 0 9 9 9 9 9 9 9 19 20 2 19 19 19 19 19 19 1 19 19 19 1 19 19 19 19 19 19 1 19 19 19 1 19 19 1 19 19 19 1 20 19 19 19 19 19 1 20 Time-scale (Years)

1982 2001 2003 1980 1992 1991 1994 1978 1969 1995 1977 1976 1973 1986 1985 1983 1997 1984 1975 1993 1998 1967 1974 1968 1971 1990 1972 1964 1970 1999 1979 1981 2002 1966 1996 1987 1988 1965 1989 2000

Figure 5-3 Trend in Peak Flood discharge level in 40 years time-period

65 RECONSTRUCTION OF 2003 DAYA RIVER FLOOD, USING MULTI-RESOLUTION AND MULTI-TEMPORAL SATELLITE IMAGERY

Gauge in Metre for 40 years at Naraj Gauging site

31 30 Danger level at Naraj: 26.52 m 3 2 29 y = -7E-05x + 0.0064x - 0.2007x + 28.18 28 R2 = 0.2022 27 26 25 24 23 22 21 20 19 e

tr 18 17 Me

n 16 i 15 14 uge 13 Ga 12 11 10 9 8 7 6 5 4 3 2 1 0

4 5 7 8 9 0 1 2 3 4 5 6 7 8 0 1 2 4 5 6 8 9 0 2 3 4 5 6 7 8 9 0 1 2 3 5 6 7 6 66 6 7 7 7 7 7 79 8 8 83 8 8 87 9 91 9 9 9 9 0 0 04 0 0 08 96 9 96 96 97 97 97 97 9 98 9 98 9 98 98 9 99 99 99 99 00 00 0 00 0 1 19 1 1 1 19 19 1 1 19 19 1 1 19 19 1 1 19 19 1 1 19 19 1 1 1 19 1 1 1 19 19 1 1 19 19 2 2 20 20 2 2 20 20 2 Time scale (years) Gauge in Mt. Polynomial fit: forecasting the trend for 5 years ahead

Figure 5-4 Trend in Flood stage level (gauge reading) for 40 years time-period at Naraj Gauging site

Trend in the measured gauge flood height over past 40 years at Naraj Railway Bridge gauging site shows that when the trend of gauging water height fits into a 2nd order polynomial, the level of flood water height for the next 5 year are predicted to be at the danger level of 26.52 m which may cause flooding at some part of the study area. This could be verified and concluded as in 2004 and 2005, there is no a significant flooding event took place at these areas. Only a small local flooding due to monsoon rain is reported to be occurred during monsoon season in a small pocket of land (News paper report only)

5.1.1.2. Determination of occurrence of Flood in Past 40 years

Using the past 40 year maximum peak discharge at Naraj Railway bridge gauging site, occurrence of the flood of varying magnitude has been done. The classification scheme used is as per the study carried out by Flood cell, Bhubaneshwar. The range of instantaneous discharge corresponding to various magnitude of flood is discussed in section 4.1.2. Discharge at Madhipur gauging site is calculated using the discharge at Naraj gauging site according to the percentage distribution of discharge at various locations. Basically, when 100% of discharge is there at Naraj, only 4.4% of discharge is expected at Daya river system. This has been verified by the Flood cell, Bhubaneshwar. Accordingly, depending upon the discharge at Naraj, it has been categorise into 4 categories i.e. HIGH, LOW, MEDIUM and NOFLOOD. This categories have been used to classify the 40 years flood data. The table below shows the detail report on the occurrence of flood as per the classification scheme of Flood cell, Bhubaneshwar.

Table 5-1Occurrence of different magnitude of Flood in 40 year time-period (descending order)

66 RECONSTRUCTION OF 2003 DAYA RIVER FLOOD, USING MULTI-RESOLUTION AND MULTI-TEMPORAL SATELLITE IMAGERY

Estimated Discharge Magnitude of flood

Discharge in in Cumecs (at (as per Flood Flood (m) Cumecs (at Naraj Madhipur gauging Cell,Bhubaneshwar Year Date Gauge Gauging site) site) – 4.4% of (4) ) (1) (2) (3) (4) (5) (6) 1982 31.08.1982 28.52 44750 1534.93 HIGH 2001 20.07.2001 27.21 39886.69 1368.11 HIGH 2003 30.08.2003 27.74 38222.96 1311.05 HIGH 1980 22.09.1980 27.8 34748 1191.86 HIGH 1992 21.08.1992 27.37 34218.8 1173.70 HIGH 1991 14.08.1991 27.13 33028 1132.86 MEDIUM 1994 06.09.1994 27.22 31576 1083.06 MEDIUM 1978 29.08.1978 26.96 27865 955.77 LOW 1969 01.08.1969 27.02 27664 948.88 LOW

1995 25.07.1995 26.72 26776.1 918.42 LOW

1977 14.09.1977 26.73 26464 907.72 LOW 1976 15.08.1976 26.73 26427 906.45 LOW 1973 28.09.1973 26.87 26245 900.20 LOW 1986 22.07.1986 26.63 25493 874.41 LOW 1985 07.08.1985 26.62 25213 864.81 LOW 1983 09.09.1983 26.61 25088 860.52 LOW 1997 06.08.1997 26.52 24127.08 827.56 LOW

1984 17.08.1984 26.45 23383 802.04 LOW 1975 24.08.1975 26.46 23146 793.91 LOW 1993 28.08.1993 25.99 23064.5 791.11 LOW 1998 13.09.1998 26.26 22907.35 785.72 LOW 1967 07.08.1967 27.08 22145 759.57 NO FLOOD 1974 19.08.1974 26.37 21577 740.09 NO FLOOD

1968 16.08.1968 26.15 21342 732.03 NO FLOOD 1971 10.08.1971 26.37 21227 728.09 NO FLOOD 1990 06.09.1990 26.19 20622.83 707.36 NO FLOOD 1972 15.09.1972 26.09 20210 693.20 NO FLOOD 1964 08.07.1964 29.41 19833 680.27 NO FLOOD 1970 28.08.1970 26.49 18520 635.24 NO FLOOD 1999 11.08.1999 25.64 17972.12 616.44 NO FLOOD

1979 10.08.1979 25.66 17716 607.66 NO FLOOD 1981 11.08.1981 25.66 17532 601.35 NO FLOOD 2002 14.09.2002 25.46 16632.59 570.50 NO FLOOD 1966 31.07.1966 28.74 16562 568.08 NO FLOOD 1996 23.08.1996 24.88 13213.07 453.21 NO FLOOD 1987 24.07.1987 24.52 9813 336.59 NO FLOOD 1988 09.08.1988 24.35 8951.03 307.02 NO FLOOD

1965 31.07.1965 27.13 8864 304.04 NO FLOOD 1989 19.08.1989 23.8 6563 225.11 NO FLOOD 2000 28.07.2000 23.45 5049.53 173.20 NO FLOOD

As per the Flood cell, Bhubaneshwar report on model study, classification of flood type is made accordingly:

67 RECONSTRUCTION OF 2003 DAYA RIVER FLOOD, USING MULTI-RESOLUTION AND MULTI-TEMPORAL SATELLITE IMAGERY

a) If discharge at Naraj is greater than 33979 cumec, then it’s considered as “HIGH”. b) If discharge at Naraj is greater than 28310 cumec but less than 33979, it’s considered as “MEDIUM” c) If the discharge at Naraj is greater than 22648, but less than 28310, then it’s considered as “LOW” d) If the discharge at Naraj is less than 22648 cumec, then it’s considered as “NO FLOOD”.

It can be concluded that studying the past flood event indicate the frequency of occurrence of flood at certain region. Above all, this analysis help us to a certain extent to determine whether the flood- producing mechanisms in the past have been same or whether there is a deviation in the trend observed.

5.1.1.3. Determination of time shift between the highest flood level and the acquisition date of RADARSAT imagery and that of Optical imagery (ASTER, IRS-PAN and LISS-III)

When using a satellite data for studies related to dynamic phenomenon like Flood, it’s very important to know the lag time between the satellite acquisition time and the day when the real phenomenon took place. To determine the maximum inundation extent, it’s required to acquire the satellite data corresponding to the exact date of peak flooding observed in the field, which in reality is not possible due to temporal constraint and also due to clouds. Here, in this study an attempt has been made to determine the time shift between the highest flood level and the acquisition date of each dataset used in this study. Date and time of satellite data acquisition is of great importance for an investigation based on satellite data. Examining the time shift between the highest flood levels as observed/measured in the field and the acquisition date of satellite dataset used in the study, its is obvious that the acquisition of the satellite data i.e. both the Microwave and optical data has some delay in regards to flood peak. This shift in time can be observed from the Figure (5-5),(5-6) and (5-7) which is being plotted for three different gauging site namely Madhipur, Kanti and Kanas. All the three gauging site has recorded a different local peak flood depending upon their location with respect to the pattern of inundation extent at various locations.

With regards to Madhipur site, the delay is 2 days, 5 days and 9days for multi-temporal RADARSAT of 4th, 11th and 13th respectively after the peak flood (12.185 m) on 2-09-2003. With respect to Optical datasets of IRS-1C/1D, Pan and LISS-III, the delay is about 6 days. A graph plotted in Figure: 5-7 with Flood height and date, indicates the wave propagation and demonstrate the acquired RADARSAT and Optical images during the flooding event.

68 RECONSTRUCTION OF 2003 DAYA RIVER FLOOD, USING MULTI-RESOLUTION AND MULTI-TEMPORAL SATELLITE IMAGERY

Maximum Peak Height 12.185 M Madhipur 20°-7'-18"N / 85º-48´-6˝E e r 11.535 Met ), L

S Danger Level 11.3 M

M IRS-PAN/ LISSIII (

l PASSES ON 08-09-

ve 2003 le

a RADARSAT PASSES

e 10.535 ON 04-09-2003 RADARSAT PASSES

an S ON 11-09-2003 Me / l ve Le 9.535 ro e Ze

Madhipur 20°-7'-18"N / ght abov i 8.535 85º-48´-6˝E he

r te Wa

7.535 3 3 3 3 3 3 3 3 3 2003 2003 2003 200 200 200 200 200 200 200 200 200 2003 2003 - - 8 8 0 0 1-Sep- 2-Sep- 3-Sep- 4-Sep- 5-Sep- 6-Sep- 7-Sep- 8-Sep- 9-Sep- 30- 31- 10-Sep- 11-Sep- 12-Sep- Days

Figure 5-5 Time shift between highest flood level and the flood situation registered by the satellite at Madhipur Gauging site In case of Kanas, the peak flood occurred on 6-09-2003. Hence, there is advance of 2 day acquisition in case of 4-09-2003 RADARSAT imagery and 5 days, 7 days, 15 days delay in case of 11th,13th and 20th September RADARSAT imageries. Aster data has a time shift of 16 days from the actual maximum peak height which is about 5.1 metre. Similarly, LISS-III and Pan has a time shift of about 2 days as can be seen from the graph of flood propagation.

Kanas 20°-6'-27"N/85°-38'-27"E

5.76 RADARSAT PASSES Maximum Peak Height 5.1 M e

r ON 04-09-2003 t RADARSAT PASSES ON 11-09-

e 5.26 2003 M ) L Danger Level 4.75 M S 4.76 M ( l RADARSAT eve 4.26 PASSES

ea l ON 20-09-2003 S

n 3.76 PAN /LISS-III a

e PASSES RADARSAT

M Kanas 20°-6'-

/ PASSES l ON 08-09-2003 27"N/85°-38'-27"E 3.26 ON 13-09-2003

eve l - o r 2.76 e ve Z

o 2.26 b ASTER PASSES a

t ON 21 -09-2003 h

g i 1.76 e h r

e t

a 1.26 W 0.76 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 03 03 0 0 0 0 0 0 0

03 0 t t t t t t t t t g pt pt 03 pt 0 pt 0 pt 0 pt 0 pt 0 pt 0 pt 0 pt 0 pt 0 pt 0 pt 0 pt 0 pt 0 p p p p p p p p ug 03 ug 03 ug 03 ug 03 e e e e e e e e e Au A A A A S t Se Se Se Se Se Se Se Se Se Se Se Se Se Se h h h h t s h S h S h S h S h S h S d S t t t t h h h h h h h h h h h h s t t t t t t t t t t t t rd 1 nd S 1st Sep 4t 5t 6t 7t 8t 9t 3rd 3 3 2n 27 28 29 30 21 10 11 12 13 14 15 16 17 18 19 20 24 2 22 Days Figure 5-6 Time shift between highest flood level and the flood situation registered by the satellite at Kanas Gauging site

69 RECONSTRUCTION OF 2003 DAYA RIVER FLOOD, USING MULTI-RESOLUTION AND MULTI-TEMPORAL SATELLITE IMAGERY

Kanti- 20-6-27N/85-46-09E

e 13.355 r t e

M , PAN / LISS-III PASSES evel

l 11.855 RADARSAT PASSES ON 04-09-2003 ON 08-09-2003 RADARSAT PASSES ON 11-09- sea RADARSAT

ean PASSES ON 13-09-

M 10.355 / level

o r Danger Level 9.62 M e 8.855 ve Z

o ab

t

h 7.355 Kanti (water level-1) g i

e Kanti (water level-2) h r e t a

W 5.855

) 3 3 ) ) ) 3 3 3 3 3 3 3 3 3 s 0 0 s s s 0 0 0 0 0 0 0 0 0 r - - r r r ------H H H g p H p p p p p p p p p 0 u e 0 0 e e e e e e e e e 0 0 0 0 . A S 0 . . S S S S S S S S S ------0 1 1 5 3 0 5 6 7 8 9 0 1 2 3 1 1 2 3 1 , , 1 1 1 1 , , s s s s r r r r H H H H 0 0 0 0 3 0 0 . . .0 . 0 0 9 6 1 0 0 0 ( ( ( ( 3 3 0 3 3 0 0 0 0 - - 2 0 2 g p - u e p - A S e p - e 0 - S 2 - S 3 3 - 4 Days

Figure 5-7 Time shift between highest flood level and the flood situation registered by the satellite at Kanti Gauging site In case of Kanti gauging site too, the peak flood height is on 30th August 2003 and 2nd September 2003. It is because there was two observations made in Kanti at different interval of time for same day. It can be seen from the two curves that are shown in Figure: 5-7. Time shift of 5 days and 9days are observed for 4th and 8th September RADARSAT imagery and 12 days for IRS-IC/ID (LISS-III and Pan) obtained on 8th September 2003.

A combined flood height for gauging site at Madhipur, Kanti and Kanas from 27thAugust – 24th September 2003 is as shown in Figure 5-8 below. This indicates the wave propagation during the time of data collection/acquisition and demonstrates the ground flooding situation satellite pass.

Measurement of Water height for the water gauges at Madhipur,Kanti and Kanas from 27th August 2003-24th September 2003 250 RADARSAT PASS AT (SEPT 04 2003) m) m) 200 c c PAN/LISS-III PASS AT (SEPT 08 2003) ( ( ASTER PASS AT SEPT 221 22003 150 evel evel RADARSAT PASS AT (SEPT 11 2003) er l er l g g 100 RADARSAT PASS AT SEPT 13 2003 Dan Dan w w 50 o o l l RADARSAT PASS AT SEPT 20 2003

e RADARSAT PASS AT SEPT 20 2003 e b b 0 d d n n -50 ve a ve a o o ab ab

t t -100 h h g g i i e e -150 r h r h e e t t a a -200 W W Madhipur -250 Kanas Kanti

3 3 3 3 3 3 0 0 3 0 3 0 3 3 Madhiipur 0 3 0 3 0 3 0 0 3 Kanas 0 Kanas 0 3 0 0 0 3 0 0 3 0 3 0 0 3 2 0 0 3 2 0 3 2 0 0 0 3 2 0 3 2 0 0 3 2 0 0 3 0 0 3 t 2 0 0 3 t 2 0 0 t 0 0 3 t 2 2 0 0 3 t 2 0 0 2 0 0 s r 2 0 0 s r 2 0 0 s r 2 0 0 s 2 0 0 s r r 2 0 r 2 0 u r 2 u e r 2 0 0 u e r 2 u e r 2 0 u e r 2 0 e r 2 e r 2 g e r 2 g b e r 2 g b e g b r 2 g e e r 2 b b e r b e r u b e r u b e r u b r u b e r u b e m b e r m b e m b e A m m b e e A m b A e m b e A m b e A e m b e m b t e e m b t e m b t e m b m h t t e m b h t t e t e m h p t e m t h p t e m t p e m t t t e m t s p p t e m p t e m e p t e m p t e m 7 e p e 8 e p t t e 9 e p 0 1 e p t e e e p t e p t e 2 e p t e 2 S e p t 2 S e e p t 3 3 S e p t S S e p S e p S e p p t S e S e p S e p S e d S e s d S e h S e h S e h S r h S e t t h S n t h S 1 t h h S t S t h S t h S 3 4 t h S 2 5 t h 6 t h 7 t h 8 t h 9 h t t h 0 1 t 2 t d 3 t 4 t s d 5 h 1 6 7 1 r t 1 8 n 1 9 1 0 1 1 1 1 1 3 4 1 2 2 2

2 2 Days 2

Figure 5-8 Showing Flood wave propagation during the time of data acquisitions

70 RECONSTRUCTION OF 2003 DAYA RIVER FLOOD, USING MULTI-RESOLUTION AND MULTI-TEMPORAL SATELLITE IMAGERY

5.1.2. Geomorphological analysis- geomorphic units inundation extent determination

In this study, the inundation patterns which correspond to each geomorphic unit are made. The four multi-temporal RADARSAT imagery of 50 metre dB value are used to identify the inundation extent in all the dates. The visually interpreted inundation map of 4th, 11th, 13th and 20th are overlay/ crossed with Geomorphological map to determine the areal extent of inundation on each geomorphic unit. This will help us to know the nature of units which are more prone to flooding i.e. where flood water used to accumulate for a longer period of time. Moreover, pattern of flooding extent from 4th to 20th September help us to know the dynamics of flood water flow within that short period of days at all location. This generated Flood evolution map, which indicates the frequency of inundation within geomorphic unit, gives inundation extent pattern for each geomorphic units. Area of inundation corresponding to each units are derived for each dates, are as shown in Table 5-2 and Figure 5-9.

71 RECONSTRUCTION OF 2003 DAYA RIVER FLOOD, USING MULTI-RESOLUTION AND MULTI-TEMPORAL SATELLITE IMAGERY

Table 5-2 Geomorphic units inundated on each date

Geomorphic units GEOMORPHIC GEOMORPHIC GEOMORPHIC GEOMORPHIC (present in Study AREA AREA AREA AREA area) INUNDATED INUNDATED INUNDATED INUNDATED

SEPT. 04 SEPT.11 SEPT.13 SEPT. 20 (Inundation Extent in km2) Paleo/abandoned 7.855 10.5725 6.385 9.895 channel(younger) Lateritic Upland 9.3425 21.6025 12.0225 14.04 Inselberg 0 0.23 0.025 0.19 Pediment 0.0875 0.0275 0.12 0.0875 Flood plain 0 5.3775 4.425 1.72 Denudational 0.4075 4.0025 1.665 0.7525 Hills-Small Natural Levee 0.3725 1.7775 0.89 0.3875 MudFlat(Young) 0.46 33.5575 32.3025 0.73 Older Mud Flat 305.1075 356.765 301.89 302.5175 Linear 0 0.0175 0 0 Ridge/Dyke Deltaic plain 66.325 70.2125 33.435 24.0675 Buried Pediment- 2.395 7.96 1.525 0.51 Medium Valley Fill 0.77 0.6525 0.0225 0.22 Back swamp 4.2475 6.5375 3.1925 3.6 Alluvial Plain 63 64.2225 55.015 30.5125 (Younger) Water Body 16.4825 15.3175 17.705 17.695 Abandoned 0.7025 0.655 0.4325 0.3 channel Residual Hill 0.2325 0.01 0 0 Channel Bar 0.0875 0.1225 0.115 0.1175 Buried Pediment- 15.28 21.045 9.515 6.0875 Shallow

Almost all the geomorphic unit present in the study area are inundated by flood water (as shown in figure) except some units which are at higher elevation i.e. due to topography. The most inundated unit which is prone to flooding is Older Mud Flat which has a inundation area of 305.1075, 356.765, 301.8 and 302.5 km2 on 4th, 11th,13th and 20th September respectively. Residual hills, Inselberg, Linear ridge/Dyke being a geomorphic unit at higher elevation are least affected by flooding. Flood plain area near the river channel, on the left bank of Daya is not affected by flooding on 4th but it got inundated on 11th and start receding on 13th and reduces it’s extent to 1.72 km2 on 20th September 2003. Hence, within this time-period flooding reaches it’s peak on 11th and start receding afterward (as can be seen from the visual interpreted map). As can be seen from the statistics of inundation on each date, the highest inundation occurs on 11th September for each of the geomorphic units. Moreover, from the

72 RECONSTRUCTION OF 2003 DAYA RIVER FLOOD, USING MULTI-RESOLUTION AND MULTI-TEMPORAL SATELLITE IMAGERY

graph (Figure 5-9 ) it can be clearly concluded that the older mudflat, mudflat (young), deltaic plain which constitute a larger portion study area are continuously inundated throughout the time period of flooding event.

Trend of Geomorphic units inundated (derived from multi-temporal RADARSAT of 4th,11th,13th and 20th September 2003)

375 360 345

) 330 315 m

K 300 . 285 q

S 270

n 255 i 240

a

e 225 r 210 A

( 195

t 180 n 165 e t 150 x 135 120 d E 105

de 90 75 oo 60 Fl 45 30 15 0 l t l m ll ) l ll r rg n in a e g) lat ke in u i r e i a w ge nd e a m v n F y la i F dy H llo n la e e u d y mp ge o nn l l B a p lb im pl -S o d /D e e a n a e ou e d ls Le Y u c p M ll w r B h n h (y U s od l l ( ge ai - s ou e idua -S c In Pe o ra t r M id lt nt Va Y t s an el iti Fl Hi u la e e ck ( d c e n l t R D a n Wa e R ent ter a dF ld r ime B i Ch m an n Na u O d a on i h La tio ea Pl d d c a M in Pe l n e d L ia a P ed u d v d n n ie u Ab e o r l ri nd De Bu Al a Bu ab o/ le a p Geomorphic units inundated GSeeopmt.4or 20ph03ic unitsGeomorphic units inundated Sept. 11 2003 Geomorphic units inundated Sept.13 2003 Geomorphic units inundated Sept.20 2003

Figure 5-9 Trend of geomorphic units inundated on each date

Geomorphic unit inundated: September 04

Figure 5-10 Geomorphic units inundated on September 04

73 RECONSTRUCTION OF 2003 DAYA RIVER FLOOD, USING MULTI-RESOLUTION AND MULTI-TEMPORAL SATELLITE IMAGERY

Geomorphic unit inundated: September 11

Figure 5-11 Geomorphic units inundated on September 11

Geomorphic unit inundated: September 13

Figure 5-12 Geomorphic units inundated on September 13

Geomorphic unit inundated: September 20

Figure 5-13 Geomorphic units inundated on September 20

74 RECONSTRUCTION OF 2003 DAYA RIVER FLOOD, USING MULTI-RESOLUTION AND MULTI-TEMPORAL SATELLITE IMAGERY

Detailed analysis on inundation of each of geomorphic unit is made. It’s found that various geomorphic units have different duration of flooding. From the Flood Evolution map (Figure:5-14) generated by using the four multi-temporal RADARSAT satellite images, different parts of inundation extent can be classified into different categories depending upon the number of day’s the flood water stagnated. This layer when integrated with the geomorphic unit map results into a geomorphic pattern inundation map. The statistics i.e. inundation extent corresponding to various unit w.r.t. duration of inundation, which is derived from geomorphic inundation pattern map is as shown in Table 5-4 and it’s graphical representation in Figure 5-15. A systematic and in details analysis and interpretation of RADARSAT imagery with that of updated geomorphological map helps us to reach to this concluding remarks. Thus this analysis help us to know the most and the least geomorphic unit present in the area which is liable to inundation for a longer and shorter duration, thereby contributing for an accurate floodplain zoning plan to prevent loss of life and materials due to flood event in near future. This highlights the need of proper understanding the geomorphology of an area which in turn helps in taking a proper decision regarding management and mitigation of Flood related hazards. As a whole, this analysis helps us in knowing the constraints and potentials of flooding zone in and around Daya river system taking into consideration the various geomorphic units present in the area.

Figure 5-14 Flood Evolution map showing the propagation of flooding pattern derived using multi- temporal RADARSAT imagery # Flood evolution map demonstrate dynamics of flooding situation i.e. the flood decrease and increase pattern using flood boundaries for all different dates obtained through visual interpretation. Areas that were affected the longest are Old Mud Flat unit, as can be seen from statistical Table 5-3.

75 RECONSTRUCTION OF 2003 DAYA RIVER FLOOD, USING MULTI-RESOLUTION AND MULTI-TEMPORAL SATELLITE IMAGERY

Table 5-3 Inundation areal extent pattern on each geomorphic units (in km2)

Flooded on 4th Duration of then Inundation extent recede Flooded Flooded Flooded Flooded Flooded Flooded Floode Flooded Flooded Flooded Flooded Flooded Flooded on 11th Non- only on only on only on only on only on only on d from from started only on only on only on only on then Flooded from 4th till 20th Flooded 4th and 4th and 4th and 11th and 11th and 13th and 4th till 11th till from 11th 4th 11th 13th 20th continuo 11th 13th 20th 13th 20th 20th 11th 20th till 20th usly Geomorphic unit flooded upto 20th 12.102 Deltaic plain 135.9275 10.6575 10.48 0.4025 2.1675 20.9925 0.2025 0.4325 1.935 0.6125 0.595 3.44 0.445 0.915 11.3425 5 Flood plain 0.6675 2.1475 0.1375 0.5 0.085 0.0025 0.65 0.39 0.0125 0.2775 0.0075 1.92 0.08 0.03 0.0025 1.16

Channel Bar 0.005 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Alluvial Plain 23.272 15.51 2.4125 2.2975 0.3725 0.39 5.8225 0.4875 0.105 0.83 0.4225 0.17 0.2275 0.2975 0.6275 27.085 (Younger) 5 Buried Pediment- 91.4225 2.3725 6.1025 0.2975 0.215 4.49 0.0375 0.0375 1.0625 0.4625 0.0275 3.125 0.095 0.015 0.225 5.0425 Shallow Back swamp 18.225 1.0325 2.625 0.13 0.7725 0.675 0.02 0.0325 0.0925 0.075 0.1225 0.355 0.055 0.1325 0.315 1.2375

Pediment 0.4425 0.0025 0.0025 0.02 0.0475 0 0 0 0 0 0 0 0 0 0 0 Buried Pediment- 42.36 0.5675 5.2425 0.0425 0.0625 0.41 0.005 0.0175 0.255 0.03 1.3425 0.0025 0.005 0.1125 0 0 Medium Lateritic Upland 43.0325 0.715 5.7475 1.02 2.01 0.8925 0.0475 0.04 1.93 3.43 0.2925 0.995 0.23 0.03 1.3175 6.5425 Valley Fill 1.2675 0.1275 0.005 0.0225 0.4275 0.01 0.015 0.21 0 0 0 0 0 0 0 0 Inselb erg 4.975 0.0525 0.0425 0.0275 0.1475 0.005 0 0 0 0 0 0 0 0 0 Linear Ridge/Dyke 0.1925 0.0225 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Denudational Hills- 10.1125 0.02 2.55 0.8275 0.05 0.1675 0.5825 0.21 0.0175 0.2125 0.26 0 0 0 0 0 Small Abandoned 5.3975 0.1125 0.075 0.0175 0.035 0.0875 0.015 0.02 0.005 0.26 0.0975 0.0075 0.0025 0.1075 0 0 channel Residu al Hill 0.46 0.1275 0.0025 0 0 0 0 0 0 0 0 0 0 0 0 0 Natural Leeve 2.9975 0.0175 0.47 0.0425 0.3025 0.005 0.0225 0.055 0.0075 0.0025 0.5 0.0025 0.01 0.045 0 0 35.162 Older Mud Flat 74.2675 6.115 17.925 4.3125 7.3375 10.89 1.8325 2.0825 8.02 17.6 5.66 18.7225 7.2925 20.735 209.475 5 paleo/abandoned 0.2975 0.0025 0.685 0.0025 0.275 0.26 0.0025 0.1725 0.0525 1.505 0.0825 0.2575 1.4925 0.0325 0.535 5.1125 channel(younger)

MudFlat(Young) 6.565 0.18 2.64 0.3025 0.8675 0.26 0.285 0.49 0.71 1.82 1.23 0.56 1.42 3.81 3.16 20.7225

* Statistics in Bold indicates the maximum inundated geomorphic units on observed date.

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Pattern of Inundations in each Geomorphic units

39 36 33 30 Km) . q 27 (S

24 Area

t 21 n e t 18 Ex

15 12 9 Inundation 6 3 0

y

l ly ly ly ly ly ly ly ly ly m m m s l o o o u 20th fr d on fr fr ded 20th ll th th th th th 11th th and th and h and th and h and then ll uo ed on ed on ed on ed on ed on ed on ed on ed on ed on ll 11th 13th 20th n 20 20 13 20 13 i th ti on 4th Floo on on on oded oded oode arted 4th then th ti nt 1 ood ood ood ood ood ood ood ood ood t 11th l l l l l l l l l recede on on 4t on 4t 1 o Fl 1 s on 13 on 11 on 11 4th ti F F F F F F F F F c 1 Flo Flo Geomorphic units Deltaic Plain Flood Plain Channel Bar Alluvial Plain (Younger) Buried Pediment-Shallow Back swamp Pediment Buried Pediment-Medium Lateritic Upland Valley Fill Inselberg Linear Ridge/Dyke Denudational Hills-Small Abandoned channel Residual Hill Natural Leeve Older Mud Flat paleo/abandoned channel(younge MudFlat(Young)

Figure 5-15 Pattern of inundation in each geomorphic unit

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5.2. Remote Sensing Data Analysis

When a decision is made to classify an image, one can use visual methods, automated methods, or a combination of the two. Here, both the visual methods and automated/digital methods are employed to extract the flood inundation extent for preparation of a Flood map using Microwave and Optical dataset.

5.2.1. Visual Interpretation

Visual interpretation is one of the oldest techniques, which is accurate (Sanyal,2005) and reliable, though time consuming, which is explored here to identify the land –water boundary extent from various dataset involved in this study. A flood map is prepared in which the inundated extent obtained from all the dataset area compared to see whether there is any appreciable changes in land-boundary in various dataset having different spatial resolution and which are multi-temporal. Moreover, the visual interpretation of Microwave and Optical data seems to be an appropriate method to get a quick view of the flood situation and to derive the boundary of flooded region from multi-temporal and multi- resolution dataset.

5.2.1.1. Analysis of Multi-temporal and Multi-resolution RADARSAT imagery

Only visual interpretation was carried out in 50 m dB and 100m DN image of multi-temporal RADARSAT imagery. For an accurate visual interpretation, the quality of the image used for interpretation have a great effect on the results. Here contrast of the image play an important role in differentiating and identifying the features distinctly. Moreover, being a microwave data, despite its ease to identify water bodies easily, due to presence of speckle, hinders in correct delineation of land –water boundary extent to an accurate extent. From the image used, identification of calm water bodies is quite easy, but those region of water bodies which are contaminated by sediment and other impurities, there tonal variation changes, hence making it difficult to demarcate the land-water extent. Moreover, interpretation of SAR imagery, however, is rather difficult, because the human eye has only a restricted differentiation ability for grey values. Additionally, some backscatter values of flooded surfaces are not very characteristic at all, thereby making it difficult to delineate the extent of land- water boundary, in case of RADARSAT (50 m, DN value). The two dataset used are of different beam mode having different Spatial resolution of 50m dB and 100m DN, and the inundation extent obtained correspondingly are presented herewith showing the deviation in extent from 50 m dB image.

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Table 5-4 Inundation extent from RADARSAT imagery

Data Description RADARSAT RADARSAT Percentage deviation of (Scan SAR Narrow) (Scan SAR Wide) inundation extent from Date of Acquisition 50m dB value 100m 50m dB (backscatter (1) (backscatter DN value co-efficient) coefficient) (km2) (2) (km2) (3) (%) (4) 4-09-2003 496.28 614.6 23.84 11-09-2003 583.61 650 11.37 13-09-2003 450.86 520.95 15.54 20-09-2003 418.005 501.44 19.96

The inundation extent map obtained by visual interpretation of multi-temporal and multi-resolution RADARSAT imagery is as shown below:

Figure 5-16 Visual interpreted inundation map of Multi-temporal RADARSAT imagery As can be seen from the images and statistics of the inundation area for the multi-date, 100 m Scan SAR wide is giving more areal inundation extent in all date. The highest extent is seen on 11th September 2003 and that of lowest on 20th September 2003 in both the cases. Percentage deviation of areal extent of 100 m imagery from that of 50 m imagery is shown in column (4) of Table 5-4. The deviation ranges from 11% to 23% considering all the four multi-date imagery.

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5.2.1.2. Analysis of inundation extent extracted from IRS-1C/1D Satellite imagery

The analysis results from the visually interpreted i.e. onscreen digitization of optical dataset are discussed in this section. Basically, it is done to see whether there is any change in land-water boundary extent in these images which is having different spatial and spectral properties. It’s seen from the analysis that the total permanent water bodies present in the whole extent of study area is about 32.75 km2 ~33 km2 out of 1160 km2 which contributes the whole extent of study area. It’s extracted out from pre-flood imagery of January 18th 2003 and categories into one separate class before comparing the actual inundation extent observed in different imageries i.e. IRS-1C (LISS-III) having 23.5 m spatial resolution, Panchromatic having 5.8 m resolution and Pan sharpened LISS-III, which is a fused product having 5.8 m spatial resolution. The different inundation extent that could be properly extracted out visually from the images is summarized in the table below:

Table 5-5 Comparison of percentage inundation interpreted visually from optical dataset

Sl.No. Datasets Date of Data Visual Interpretation acquisition Inundation extent Percentage in Km2 inundation (%) 1 Panchromatic (5.8 m) 08-9-2003 575.04 49.57 2 Pan sharpened LISS-III (5.8 m) 08-9-2003 564.80 48.68 3 IRS-1C (LISS-III,23.5m) 08-9-2003 575.03 49.75

Figure 5-17 Visual Interpretation Map of Optical Datasets

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The following analysis of visual interpretation maps prepared is given here: i) Pan Sharpened LISS-III and LISS-III:

The water-land boundary extent in case of LISS-III visually interpreted having a spatial resolution of 23.5 m is more as compared to Pan sharpened image of 6 m. The inundation extent extracted from LISS-III (575.03 km2) is 10.23 km2 more than that of inundation extent of Pan sharpened LISS-III (564.80 Km2). In addition, the water-land boundary distance between the two inundation extents at some places like that of embankment breach areas is measured interactively. It’s found that the extent in LISS-III is 0.924 Km~ 924 metre more than that of Pan sharpened LISS-III. Differences in extent at other locations are also determined which is about 0.207 km, 0.278 km, 0.464 km etc. Hence, it could be concluded that inundation extent varies greatly at different part of the area under consideration. ii) Panchromatic and Pan Sharpened LISS-III:

In this case, the difference in water-land boundary extent is seen in some pocket of the region. The difference might be possibly due to gray representation of Pan Image, thereby making it difficult to identify small pocket of inundated region, which in turn make it difficult for an analyst to delineate the exact boundary inspite of its high spatial resolution. This limitation is overcome by using fused Pan sharpened LISS-III which represent all the features in RGB display and have a high spatial resolution like that of original Panchromatic imagery. Here, the total areal extent difference calculated is about 10.24 km2. Contradictory, in some areas during analysis, it is found that inspite of greater total areal extent in Pan, water-land boundary extent at some specific location is more in Pan-sharpened LISS-III corresponding to Pan. The difference in water-land boundary at some locations which is measured interactively by overlaying of the corresponding map gives a differences of 0.152 km, 0,374 km, 0.185 km which varies at different part of flooded/ inundation extent that is visually identified. iii) Pan and LISS-III:

In this case, almost all the land-water boundary seems to be similar at all location where inundation is visible in the imagery. This is further verified after calculating the areas of inundation on both the two visually interpreted map. There is a minor difference in inundation extent when comparing the extent in both the imagery. The areal extent in both the imagery is 575.04 km2 and 575.03 km2 respectively.

5.2.1.3. Comparison of inundation extent from Optical and RADARSAT imagery

A detailed statistics on inundation extent extracted out using Optical and RADARSAT imagery is given below: In optical, since they are of same date, the comparison is made w.r.t. spatial resolution effect on inundation extent extraction visually and the results from fused Pan sharpened LISS-III imagery.

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Table 5-6 Comparison of inundation extent by visual interpretation of optical and RADARSAT images

Sl.No. Datasets Date of Data Visual Interpretation acquisition Inundation extent Percentage in km2 inundation (%) OPTICAL DATASET

1 Panchromatic (5.8 m) 08-9-2003 575.04 49.57 2 Pan sharpened LISS-III (5.8 m) 08-9-2003 564.80 48.68 3 IRS-1C (LISS-III,23.5m) 08-9-2003 575.03 49.75 RADARSAT (Scan SAR Narrow) 50m dB value (backscatter coefficient) (km2 ) 4 RADARSAT 4-09-2003 496.28 42.77 5 RADARSAT 11-09-2003 583.61 50.30 6 RADARSAT 13-09-2003 450.86 38.86 7 RADARSAT 20-09-2003 418.005 36.03 RADARSAT (Scan SAR Narrow) 100m DN value (km2) 8 RADARSAT 4-09-2003 614.6 52.97 9 RADARSAT 11-09-2003 650 56.03 10 RADARSAT 13-09-2003 520.95 44.9 11 RADARSAT 20-09-2003 501.44 43.22

The total extent of the study area is about 1160.08 km2. Out of the total extent, the area that are inundated at various dates and as observed in various datasets are calculated and their percentage inundation is also determined to see the variation in various land-water boundary extraction across various datasets. It’s seen that in all case, the maximum inundation extent is extracted out from 11-09-2003, where it indicates that during the period from 4th to 20th September 2003, 11th Septembers got the highest flood extent. Similarly, Sep20th has the lowest extent where it indicates the decreasing trend in flooding from 11th to 20th September 2003.

5.2.1.4. Inundation Extent comparison between RADARSAT and ASTER imagery

The whole extent of study area is not available in scene of ASTER image dated 21-09-2003. Hence, only the overlapping part which falls under study area is extracted out to make a comparison. Here, the comparison is made across different spatial resolution of 15m, 50m and 100 m of ASTER, RADARSAT (dB) and RADARSAT (DN) respectively with a temporal difference of 24 hrs i.e. one day. Since, ASTER data obtained on 21-09-2003 is contaminated with cloud; only a part of study area which is free is used to delineate the land-water boundary extent and the extent obtained from it is compared with that of multi-temporal RADARSAT imagery. This gives us a clear picture regarding the superiority of microwave data over optical, when studies are carried out related to flooding, where the possibility of cloud contamination is high in specific season like monsoon season. ASTER, in spite

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of having a resolution of 15 m, is not able to give a precise land-water boundary extent when compared to RADARSAT having spatial resolution of 50m and 100 m. The total subset area for comparison purpose is 460 km2. The area of inundation extent within the common comparison subset is summarised in the table given below:

Table 5-7 Area of inundation extent in ASTER and RADARSAT

Data Specification Inundation Extent in Percentage inundated (within (km2) the area under consideration) ASTER (15m)- 21-09-2003 119.174 25.90 RADARSAT (50 m)-dB – 20- 09-2003 328.10 71.32 RADARSAT (50 m)-DN – 20- 09-2003 349.68 76.01

It can be seen from statistics that the extent in case of both the RADARSAT imagery are almost similar. There is a slight difference in areal extent which is about 21.58 km2. But in case of ASTER imagery, due to contamination of cloud the areal extent that could be extracted out is less i.e. the land- water boundary extent extraction is not possible in addition to this, the receding water might have reduced the flood extent.

5.2.2. Digital analysis and extraction of Inundation area

Besides Visual method, Digital Methods were also explored here to see whether up to what accuracy and reliability inundation extent could be extracted and to study the variation in extent when comparing across multi-sensor and multi-temporal RADARSAT data. This in turn will help us to find a quick and operational method for flood mapping. The inundation extent i.e. flood map which are prepared by various techniques like thresholding, unsupervised, supervised, PCA (Principal Component Analysis) and textural based-classification are analysed and see that whether there is any appreciable changes in land-water boundary in all multi-date imagery. Results from various techniques mentioned above, which is applied to three different dataset for extracting the inundation extent are as presented in the Table 5-9,5-10,5-11 below: and a detail analysis for each techniques is described in subsequent sections.

Table 5-8 Comparison chart of inundation extent for RADARSAT imagery(Scan SAR Narrow, 50 m, dB)

Flood inundation extent of Multi-temporal RADARSAT imagery

83 Digital Classification Techniques

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Table 5-9 Comparison chart of inundation extent for RADARSAT imagery (50, DN value)

Date of Data Used Digital Classification Techniques

Image Thresholding/Density Unsupervised Supervised Classification acquisition Slicing (Iso data clustering)

Maximum Minimum Area of Inundation extent in km2 Likelihood Distance classifier 4-09-2003 RADARSAT 434.41 414.22 665.85 813.12 (Scan SAR 11-09-2003 566.71 566.71 880.81 429.74 Narrow) 13-09-2003 50m, 493.00 499.44 518.54 574.81 DN value 20-09-2003 413.76 466.91 905.42 437.78 # Total study area extent = 116008 hectares ~1160.08 km2

Table 5-10 Comparison chart of inundation extent for RADARSAT imagery (100, DN value)

Flood inundation extent of Multi-temporal RADARSAT imagery

Digital Classification Techniques 84

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The three datasets that are used in various techniques, there is an appreciable changes in areal extent given by each techniques. This is due to the fact that different technique follows a different algorithm to process the extraction of flooded area. Owing to a lack of a standard of comparison, the visual interpretation of the flooded area of dB, 50m RADARSAT image is used to compare the results from other techniques. Visual interpretation, being considered as one of the most accurate and reliable techniques to identify features in the imagery after a proper enhancement is applied to the original images, though it is time-consuming it is taken as a reference for cross comparison with the results obtained from all other digital techniques i.e. the area of flood inundation extracted out by visual interpretation is taken as reference extent of inundation for all multi-date imagery for comparing the various results generated from various techniques.

5.2.2.1. Thresholding approach

In this analysis, the dB and DN values of water bodies and non-water bodies is read from the image. The range of threshold value is assigned to extract the flood extent from other features present in the imagery. Initially the whole region is threshold into two broad classes in case of dB image but in case of DN image of 50 m and 100 m, it’s found that some part of the flooded region is merging with that of non-flooded region (comparing with the extent of inundation obtained through visual interpretation). Hence, to avoid this problem of overlapping threshold range, the whole area is again threshold into three classes’ i.e. Deep, Shallow and Non-Flooded region. The range of threshold that is assigned for each dataset is summarised as below:

1) DN < k is deep flood extent, k=< DN= k' is non-flooded region, where DN represent gray value in RADARSAT DN image of 50 m, 100m. 2) dB < K is flood extent, dB>= K is non-flooded region, where dB represent the backscatter value in RADARSAT dB image of 50 m and K is the threshold value.

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Table 5-11 Threshold range for all the multi-temporal and multi-resolution RADARSAT

Data specification 50m, dB image 50m,DN image 100m, DN image Flooded Non- Deep Shallow Non- Deep Shallow Non- flooded flooded Flooded Value of k (dB=K) Value of k and k' 04-09-2003 -13 -13 DN< 55== DN< 23== 55 <74 74 23 <37 37 11-09-2003 -7 -7 DN< 39.9== DN< 33== 39.9 N<52.79 52.79 33 <48 48 13-09-2003 -8 -8 DN< 26.9== DN< 31== 26.9 N<59.01 59.01 31 <60 60 20-09-200 -8 -8 DN< 48.31=< DN>= DN< 48== 48.31 DN<69. 69.708 48 <69 69 708

The inundation extent extracted by applying the above threshold to the dataset is presented in the form of a bar-chart as shown in Figure 5-18. Basically, the trend of flood extent from 4th to 20th is same in all the cases; only there is difference in the areal extent. For all the cases, the highest inundation extent is observed in September 11th and that of lowest in that of 20th September. This trend which is observed here is same as that is obtained from visual interpreted map. The threshold based classified inundation map is as shown in Figure: 5-19; 5-20; 5-21.

Thresholding of RADARSAT

600

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400

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Figure 5-18 Inundation areal extent (in km2) as extracted out from 3 different dataset

Figure 5-19 Threshold based classified Inundation Map of multi- temporal RADARSAT 50 m, (dB)

Figure 5-20 Threshold based classified Inundation Map of multi-temporal RADARSAT50 m,(DN)

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Figure 5-21 Threshold based classified Inundation Map of multi-temporal RADARSAT100 m,(DN)

It’s seen that the threshold based classification gives a fairly good results in extraction out the inundation extent. There is some misclassification of shallow with that of deep flooded seen in case of September 11,100 m,DN but it doesn’t affect the result as such because both the classes fall under flooded categories, when statistical comparison is made. Initially, misclassification is also seen in September 20th imagery in small pocket in the upper north-western part of the study area which is mainly due to the presence of speckle and noise in the RADAR imagery inspite of difficulty in assigning a proper threshold for each classes identified. Attempt has been made to filter and suppress the speckle and noise i.e. applying Median and Lee filters, which is generally applied in SAR imagery, then subsequently the filtered out image is classified again giving the same threshold range. The results can be seen from the classified map. In general, September 4th and 13th imagery in case of 100 m, DN gives a reliable and distinct classified land-water extent map as compared with the other two multi-date data of 13th and 20th. In case of dB image of 50 m, the threshold map corresponds to September 4th, 11th and 13th gave a fair and accurate extent when compared with visual interpreted map. In this case, applying only two broad threshold ranges could differentiate between flooded and non-flooded, without losing any region that was marked as flooded in visual classified map. Moreover, being a backscatter value, since range is also very high it’s easy to select a threshold range when compared with DN value image.

5.2.2.2. Unsupervised approach

In the light of distinct backscatter response in the RADARSAT data, an attempt is made to classify/extract the inundated area from the multi-temporal RADARSAT dataset acquired during the flood period of 4th to 20th September 2003 using unsupervised approach. Since, unsupervised classification doesn’t require the user to specify any information about the features contained in the data, the whole image is classified into 20 classes using Iso-data clustering algorithm (details discussed in section 4.2.3.3.), thereby recoding is made which finally result into two broad classified map i.e. flooded and non-flooded. The resulting images is as shown in Figure: 5-22; 5-23; 5-24.

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Figure 5-22 Classified inundation map of Multi-temporal RADARSAT 50 m, dB.

Figure 5-23 Classified inundation map of Multi-temporal RADARSAT 50 m, DN.

Figure 5-24 Classified inundation map of Multi-temporal RADARSAT 100 m, DN.

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The dynamic changes of the flooded area during the monitoring period is analysed herewith. Flooded and non-flooded estimated by Iso-data clustering for the 4 days during the flood are shown in Table 5- 13.

Table 5-12 Inundation extent by Iso-data Clustering

Inundation Extent (in km2) Date of 50 m,dB 50 m,DN 100 m,DN acquisition 4-09-2003 591.86 414.22 592.45 (51.02) (35.70) (51.07) 11-09-2003 626.61 566.71 603.78 (54.01) (48.85) (52.05) 13-09-2003 609.70 499.44 595.42 (52.56) (43.05) (51.32) 20-09-2003 367.35 466.91 413.39 (31.66) (40.25) (35.63) * The value in parathesis are percentage of inundation.

Inundation extent- Iso data clustering

700

600

500 ) Km . q

S 400 ( ed at d n

u 300 n i a Are 200

100

0 4/9/2003 11/9/2003 13/9/2003 20/9/2003 Monitoring period 50 m,dB 50 m,DN 100 m,DN

Figure 5-25 Dynamic trend of flooded areas from three different dataset

The trend of the flooded area obtained from the three dataset shows that there is an increase in flooded extent from 4th to 11th and it started decreasing on 13th September. Then suddenly, the flooded water recedes away on 20th September as can be seen on the graph. This trend could be seen on all the results obtained from three different dataset.

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When the extent of inundation obtained from the three dataset are compared with that of extent from visually interpreted, it’s seen that inundation extent from 50 m, DN is quite close to it. Datasets with same spatial resolution of 50 m, one having a pixel value and another backscatter co-efficient, gives different extent of inundation. The extent in 11th and 13th in case of dB value image is higher i.e. variation is much as compared to extent from other two dataset. Moreover, the extent in 20th September in dB value image is drastically reduced which indicate the inconsistency in Iso-data algorithm when applying across multi-resolution and multi-temporal product dataset. The percentage area inundated on all dates using the three dataset is shown in bracket in Table 5-12.

5.2.2.3. Supervised approach

The results from the supervised approach are shown in Table 5-14 and Figure: 5-26. The flood dynamic i.e. pattern of inundation extent from 4th to 20th September are analysed herewith and a comparison is made between the results obtained from two classifiers i.e. Maximum Likelihood and Minimum Distance. Moreover, the variation in extent in different dataset are observed too.

Table 5-13 Inundation extent obtained from supervised approach

Date of Acquisition Maximum Likelihood classifier Minimum Distance 50 m,dB 50 m,DN 100 m,DN 50 m,dB 50 m,DN 100 m,DN 04-09-2003 457.14 665.85 619.58 528.08 813.12 714.04

11-09-2003 586.36 880.81 586.98 691.34 429.74 749.79

13-09-2003 645.65 518.54 566.33 589.14 574.81 657.95

20-09-2003 492.20 905.42 617.14 515.02 437.78 800

Here, the variation in extent derived from Maximum Likelihood classifier is less than that of minimum distance when compared with the visually extracted extent. On the other hand, the extent in September 11th and 20th i.e. 880.81 km2 and 905.42 km2 in case of 50 m, DN is very high as compared with the extent from other datasets. Moreover, the pattern of actual inundation could not be observed in the resulted statistical analysis except in case of 50 m, dB by application of minimum distance algorithm.

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Inundation extent: Maximum Likelihood Classifer

1000

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700 ) m

q.K 600 S d ( e t 500

inunda 400 a e r A 300

200

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0 4/9/2003 11/9/2003 13/9/2003 20/9/2003 Monitoring period/Date of Image acquisition 100 m,DN 50 m,DN 50 m,dB

Figure 5-26 Distribution of inundation extent pattern in the three dataset using MLC.

Inundation Extent: Minimum Distance

900

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) 600 m K q. S 500 d ( e t da 400 nun I a e r

A 300

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0 4/9/2003 11/9/2003 13/9/2003 20/9/2003 Monitoring period/Date of acquisition 100 m,DN 50 m,DN 50 m,dB

Figure 5-27 Distribution of inundation extent pattern in the three dataset using Minimum Distance.

Derived inundation map of 50 m, dB and 50 m, DN and 100 m, DN of 11th September and 20th September are shown here in Figure: 5-27 to show the difference in extent observed using the two classifier algorithms.

The two parametric algorithms as described in section 4.2.3.4 are applied to the three different Microwave datasets. It’s seen that application of both the algorithm in RADARSAT imagery doesn’t give a satisfactory results, when compared with the extent obtained using other approaches. Basically, there is a large deviation in extent from that of visually interpreted flood map. The accuracy and quality of the extent obtained by supervised classification is hereby evaluated by referring to the mapping of visual one of the same dataset. Various misclassifications could be seen in all dataset, which could be possibly due to speckle content in RADARSAT imagery. To improve the

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misclassifications, Median and Lee filtering are applied to suppress the speckle and noise, but the results do not improved much. RADARSAT images intensities are dependent on the characteristics of the illuminated surface target as well as on the signal itself. Moreover, the information content in RADAR images are different from that of optical, i.e. interpretation of feature content is different and it depend upon the types, sizes, shapes and orientations of the scatter in the target area; moisture content of the target area; frequency and the polarization of the RADAR pulses etc. hence make it difficult in selection of a proper training site for carrying out the supervised classification. The large variation in inundation extent observed from this approach is due to unsuitability of the algorithm for processing RADARSAT imagery. Hence, advanced techniques like classification on the textural images i.e. texture based classification, derived from Original RADARSAT imagery are attempted in this study for comparison purpose. Moreover, advanced algorithm like EBIS (Evidence based Interpretation of the satellite images) should be adopted for accurate SAR classification (Oberstlader et al., 1997).

Figure 5-28 Classification results from Maximum Likelihood classifier (MLC) and Minimum Distance (MD), as applied to three different multi-temporal datasets

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As can be seen from the above classified image, the extent of flooded region varies in the three dataset. Moreover, during classification by both the algorithm, three broad classes were identified. There is no clear distinct boundary between deep and shallow inundated areas, as can be seen in all derived images. There is overlapping of the two classes and in September 11th of 50 m,dB the deep flooded/inundated area couldn’t be extracted out accurately besides all the inundated part are classified into shallow flooded region. Hence, there is no effect of this overlapping between deep and shallow flooded region for the statistical determination of the total flooded areal extent, for comparison purpose.

5.2.2.4. PCA approach

Principal component analysis is made on the three dataset i.e. 50 m, dB; 50 m, DN; and 100 m, DN. The results obtained from this approach is analysed as discussed below:

For all the datasets, 4 Principal Component Image (PCI) are generated since each dataset contains 4 separate bands where each band represent the image acquired on a each specific date i.e. 4th, 11th ,13th and 20th September 2003. In short, the multi-temporal RADARSAT images are merged to form an F.C.C. where analysis is made to see which PC component could give the most reliable and largest extent of flooded region. Moreover, how different PC can be combine to form a colour composite where the extent of inundation can be visualise and separated out easily from the rest of features present in the imagery. To increase the efficiency in image classification and interpretation of features i.e. flooded extent present in the imagery, the original images of three different datasets are transformed to PC bands which contain almost all the variance of original images. The 4 PCI generated for each datasets and they are analysed separately. An important characteristics common to all 4 PCI being generated as discussed below:

PC1 is a weighted sum or weighted average separating the information that is common to, or redundant among, the four band images, and explaining most of the variance in the four input multi- temporal RADARSAT imagery. Being a weighted sum, it looks similar to the input bands, extracting features common to the four window band images. PC2 is a weighted sum-and –difference image, highlighting where the four window band images differ the most, and explaining the majority of the remaining variance among the four input bands. PC3 and PC4 is also the weighted sum and difference image, with the remainder of the explained variance among the 4 input bands. These four PCI contains the same information as the input bands, but rearranged by separating the common and difference images.

A detail statistical description for all the PCI generated from all the dataset are discussed subsequently to understanding the variance and Eigen vector loading of each PC components. The analysis from the spectral profile of different PC and the Eigen value matrix shows that only PC1 and PC2 content the highest amount of information and variation and the separability between land water boundaries can be easily achieved in PC1 and PC2 as can be analyzed from the corresponding spectral curve. In addition, the possible colour composite of PCI for extracting the extent of inundation are discussed in this section.

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1) 50 m, dB (Multi-temporal RADARSAT imagery)

PCI generated from the 50 m, dB images are as shown in Figure 5-29 and its spectral profile in Figure: 5-30 and it’s statistical and eigen vector loading are given in Table 5-15 and 5-16.

PC1 PC2 PC3 PC4

Figure 5-29 PCI of Multi-temporal RADARSAT imagery (50 m, dB image)

B) d ( e lu a V l e x Pi

Principal component

Figure 5-30 Spectral profile of inundated area and non-inundated areas present in different PCI-50 m, dB image

From the PCI image and spectral profile, it can be seen that separation of land-water boundary extent can be made easily from PC1 and PC2. In addition, the percentage of the eigen values indicates the

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percentage of variance explained in each vector. For instance, the first principal component, PC1 given in the first row of the Table 5-16 indicates 89.127% variance.

Table 5-14 Statistics of the input bands from Multi-temporal RADARSAT 50m, dB image

Input Band Band 1 (4-09- Band 2 (11-09- Band 3 (13-09- Band 4 (20-09- 2003) 2003) 2003) 2003) Band Mean -7.213 -4.472 -6.136 -6.252 Standard 6.954 4.495 6.395 6.517 deviation of Bands

Table 5-15 Eigen vector matrix of Multi-temporal RADARSAT imagery 50 m,dB dataset

Band Band1 Band2 Band3 Band4 Eigen %

value

PC1 0.57391 0.4777 -0.63009 0.213007 135.09126 89.127

PC2 0.34146 0.45717 0.761537 0.307328 9.86314 6.507

PC3 0.533694 -0.81236 0.1441519 -0.829331 3.43437 2.265

PC4 0.518839 -0.745763 0.047504 0.415192 3.1817805 2.099

2) 50 m, DN and 100 m, DN (Multi-temporal RADARSAT imagery)

Similarly, the same analysis is carried out for the other two datasets and their results are presented below.

PC1 PC2 PC3 PC4

Figure 5-31 PCI of Multi-temporal RADARSAT imagery (50 m, DN image)

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e ) u l

a v N ue (D l a V l e x i

P

Principal component

Figure 5-32 Spectral profile of inundated area and non-inundated areas present in different PCI-50 m, DN image

Table 5-16 Statistics of the input bands from Multi-temporal RADARSAT 50m, DN image

Input Band Band 1 (4-09- Band 2 (11-09- Band 3 (13-09- Band 4 (20-09- 2003) 2003) 2003) 2003) Band Mean 57.144 38.242 45.443 56.865 Standard 54.692 33.658 44.431 54.322 deviation of Bands

Table 5-17 Eigen vector matrix of Multi-temporal RADARSAT imagery 50 m, DN dataset

Band Band1 Band2 Band3 Band4 Eigen value %

PC1 0.581121 0.335114 -0.460012 0.5817088 8164.5094 90.057

PC2 -0.46789 0.501237 0.6471936 -0.3313039 418.8366 4.619

PC3 -0.65437 -0.205827 0.0595869 0.7251710 309.245 3.411

PC4 -0.123128 0.770775 -0.6049616 0.1573719 173.321 1.911

In case of PC image obtained from 100 m, DN, only PC1 give the most separability between flooded and non-flooded region as can be seen from the Spectral profile, Figure 5-33, which is plotted after

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selecting a random grey values corresponding to each classes. It’s also seen that in PC2, PC3 and PC4, there is overlapping of spectral signatures, hence the separability of the two classes is not accurate.

Figure 5-33 Spectral profile of inundated area and non-inundated areas present in different PCI -50 m, DN image

The statistical and Eigen vector loading from PCI- 100 m, DN image are given below:

Table 5-18 Statistics of the input bands from Multi-temporal RADARSAT 100m, DN image

Input Band Band 1 (4- Band 2 Band 3 Band 4 (20-09-03) 09-03) (11-09- (13-09- 03) 03) Band Mean 87.302 6.861 2.790 3.053 Standard deviation 76.233 14.070 7.799 6.074 f B d Table 5-19 Eigen vector matrix of Multi-temporal RADARSAT imagery 100 m, DN dataset

Band Band1 Band2 Band3 Band4 Eigen % value

PC1 0.4185 -0.4929 0.10735 0.7551 5817.409 88.47

PC2 0.3786 -0.04167 -0.91844 -0.1065 419.536 6.380

PC3 0.5366 -0.4529 0.31579 -0.63807 231.394 3.519

PC4 0.62724 0.74165 0.21261 0.106.607 106.607 1.621

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The final output flood inundation map which represent the maximum inundated areas and its corresponding inundated extent generated from PC analysis for the three dataset are shown below:

Figure 5-34 Final output Flood map representing the maximum inundated area as generated form PC analysis

Table 5-20 Areal extent given by each component when applied to Multi- resolution and Multi-temporal dataset

Inundation extent on Principal component images (in Sq. Km) PRINCIPAL PRINCIPAL REMARKS COMPONENT 1 COMPONENT 2

The variance in PC3 and PC4 is very

dB image (50 m, scan 552.38 445.79 low, hence it’s not able to extract the SAR Narrow) land water boundary i.e. Inundation extent. The variance in PC3 and PC4 is very low, hence it’s not able to extract the DN image( 50 m, scan 492.98 515.93 land water boundary i.e. Inundation SAR Wide) extent. The variance in PC2, PC3 and PC4 is DN image ( 100 m, 377.82 ------very low, hence it’s not able to extract scan SAR Wide) the land water boundary i.e.

Inundation extent.

Here, it can be seen that the extent given by PC1 (dB image) is deviated by 5.35 % from the inundation extent of visual interpreted approach when considering the imagery of September 11th 2003. In case of DN images also, both PC1 and PC2 are used for extracting the inundated areas for 50 m and only PC1 for 100 m, DN images.

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An attempt has been made to see how Color combination of PCI bands can be used effectively for better separation of inundation extent i.e. land water boundary separation. A colour composite of 1:3:1, 3:3:1, 1:1:3 i.e. combination of band for colour composite generation, in case of 50 m, DN image, help to identify the deeply flooded and shallow flooded extent in the study area which in turn can be visualize properly & separated out from the other features classes present. Similarly, in case of dB image many combination was tried and it was found that 1:3:1,2:2:1,2:2:1 gives a better visualization where one can delineate and identify the water bodies extent from that of other classes in the study area. Indices were generation by combining (PC1-PC3)/(PC1+PC3),( PC1-PC4)/PC3 but it doesn't give satisfactory result where one can separate out the inundation extent from other classes. The results may be due to poor Eigen vector loading in other PC component except that of PC1 and low standard deviation in the original datasets.

5.2.2.5. Textural based approach

As described in the methodology section, using time-series RADARSAT imagery, Textural based classification is carried out in this study where the various textural images generated are classified into different desired classes by giving a proper and accurate threshold. The results obtained from textural based classification are compared with that of visual interpreted inundated extent, to assess the percentage deviation for each textural measure thereby helping to identify those textural measures which could be effectively used for inundated extent extraction and as a whole for the accurate flood mapping.

Identification of a proper threshold for all the textural measures considered for this analysis, where the land-water boundary extent could be separated distinctly, becomes a main problem for deriving an accurate inundation map. The main factor behind it is that each textural image derived from the original images i.e. variance, homogeneity, entropy, etc have different range of dB and DN (gray value) for dB derived and DN derived images respectively. Hence, to avoid this problem, a standard range of threshold value corresponding to September 11th and that of September 20th are derived and it is applied to the intermediate time-series imagery of September 4th and 13th for all the dataset.

For assessing the accuracy of the inundation extent derived for each of the measures for all the dataset, the difference in percentage deviation from the inundation extent corresponding to minimum and maximum extent delineated by visual interpretation of 50 m, dB image is considered as standard comparison value i.e. inundation extent corresponding to September 20th and 11th respectively. Then the same is applies to September 4th and 13th textural derived inundation extent to see the deviation from each corresponding visually derived extent.

The main difference from that of Simple Thresholding technique is that here threshold value are required to be derived for different Textural images, before generating a classified map for each measure. The ranges of threshold value corresponding to each measures which are used for classification are shown in Table: 5-22Basically, the whole range is reclassify into three different ranges whereby it’s identified as Deep, Shallow and Non-Flooded.

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It’s found that Homogeneity, Second moment and Correlation measures derived images have high backscatter co-efficient value which correspond to inundated area as contrast to other measures derived images where inundated areas correspond to low backscatter value. Moreover, the range of backscatter value is very close in case of above mentioned measures derived texture images. On the other hand, variance and contrast have a normal range of radar brightness value ranging from 0 to 227 and 0-185 respectively.

For the September 20th imagery, in case of 50 m, dB value, the extent of inundation obtained from Contrast, Dissimilarity, Entropy and Second moment deviates negatively from the minimum extent delineated from visual interpretation of dB image dataset. And for other measures i.e. Variance, homogeneity and correlation, it deviates positively. All this depends upon the range of value contained in each measures and the threshold that is assigned correspondingly to extract the inundated region from each measures. A similar trend is expected in case of 50 m, DN image generated measures, but there is a large variation in deviation for each measures. This is possibly due to the difference in backscatter coefficient (dB) value and the brightness value (DN), within the two dataset inspite of having the same spatial resolution of 50 m. This trend could be observed in case of September 11th derived inundation map.

Hence, it could be concluded that all the inundation extent derived from each measures have a certain degree of deviation from the minimum and maximum inundation identified in time-series RADARSAT imagery which correspond to September 20th and September 11th 2003.

For, 50 m DN dataset, Contrast, Second moment and Homogeneity measures gives an extent that nearly equal/correspond to that of visually interpreted map of 20th September 2003, considering the minimum extent from the four temporal RADARSAT imagery. Similarly, considering the maximum extent of visually interpreted map of 11th September 2003, which is 583.61 km2, Homogeneity and Second moment, gives an extent of 587.26 and 584.28 km2 which is positively deviated by 0.626% and 0.20% respectively. Moreover, extent derived from Variance, Contrast and dissimilarity is within a range of 561-568 km2, which is deviated positively as well as negatively. But the percentage inundation derived from correlation measures are deviating to a large extent which is about -39.95%. Similarly, for the imagery of 4th and 13th imagery are analysed by applying the same threshold corresponding to Maximum and minimum extent derived from Visual and their variation are analysed and presented in Table 5-23

The same procedure applies for the other two dataset and the results obtained are shown in Table 5-24 and 5-25. From the Statistical table of 50 m,dB value imagery, it can be clearly found that Contrast, Dissimilarity, Entropy and Second moment comes out to be promising measures for extracting the inundation extent that correspond to minimum extent. Homogeneity and Second moment measures derived extent give a quite reliable and comparable result when compared with that of September 11th imagery where maximum extent is identified through visual interpretation considering the four time- series RADARSAT imagery.

When considering the 100 m, DN image, it is found and concluded that applying the same threshold to this dataset doesn’t hold good. The two main reason that might caused this large deviation in September 11th and 20th is that i) being the gray value correspond to this is in Brightness value, DN ii)

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and the spatial resolution of this dataset being 100 m as compared to 50 m in both the other two cases discussed above. This approach on the other hand, helps one to determine the effect of application of thresholding in original RADARSAT imagery and to that of derived textural images. For analyzing purpose, a comparison is made between the inundation extend derived from original RADARSAT and Textural based RADARSAT imagery by applying normal thresholding techniques. For analysis, only considering the extent obtained from September 11th and 20th imagery for both the cases, a comparison sheet is shown in Table 5-21.

Table 5-21 Comparison of inundation extent by applying threshold to original and Textural measures image

Thresholding application on Original RADARSAT and Textural measures imagery of 50 m,DN

Image Original Thresholding on Textural imagery RADARSAT 50m,DN acquisition date RADARSAT 50 m, DN image Homogeneity Contrast Second Moment 11-09-03 566.71 587.265 564.66 584.78 20-09-03 413.76 424.81 413.22 413.82

A very specific trend is observed where the extent of inundation derived from original RADARSAT imagery correspond exactly to that of inundation extent generated when using the Contrast measures image.

Except for the 100 m DN dataset, the inundation extent is successfully extracted out precisely from Homogeneity, Contrast, Second Moment measures. Hence, the suitability of textural measures for flood inundation mapping is studied. The textural images and their corresponding inundated extent images of September 11th, 50 m,DN and dB are shown in Figure: 5-35 and Figure:5-36 :

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Figure 5-35 Textural images for September 11th 2003 (50 m,DN)

Figure 5-36 Textural classified image for September 11th 2003, (50 m,dB)

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Table 5-22 Thresholding Range -Textural Analysis for Flood inundation extent determination- 50m (dB) 50m(DN) 100m(DN)

Extent of study area: 1160.08. Thresholding Range -Textural Analysis for Flood inundation extent determination- 50m (dB) 50m(DN) 100m(DN) Km2

Date of Variance Homogeneity* Contrast Dissimilarity Entropy Second moment* Correlation*

Acquisition

20.09.2003 Range of threshold for all the measures (MIN)

dB value range 0 - 227 0 – 0.749 0 - 185 0 - 11.48 0 - 6 0 – 0.27 -72.28 – 81 (Minimum 0.0001 – 22.07, 0.0001 – 0.2055, 0.0001 – 21.49, 0.0001 – 1.929, 0.0001 – 1.926, (Deep), 0.0001 – 0.007698, (Non- -72.28- inundation extent (Deep) (Non-Flooded); 0.2055 (Deep), 21.49 – (Deep), 1.929 – 4.697 1.926 – 4.439 (Shallow), Flooded) - 8.321 delineated from 22.07- 63.56, – 0.3807,(Shallow); 38.567, (Shallow), (Shallow), 4.697 – 4.439 – 6, (Non-Flooded) 0.007698 – 0.012081, (Shallow), (Non-Flooded) Visual (Shallow) 0.3807 – 0.749 (Deep) 38.567 – 185 11.48 (Non-Flooded) i.e. dB<4.439 0.012081 – 0.27, (Deep) -8.321– Interpretation) 63.56 – 227 i.e. dB>0.2055 (Non-Flooded) i.e. dB<4.697 i.e. dB>0.007698 -0.0001, (Shallow), 0.0001 – (Non-Flooded) i.e. dB<38.567 81,(Deep) i.e. dB< 63.56 i.e. dB>-8.321 11.09.2003 (MAX) 0 – 190 0 – 0.80 0 – 190 0 – 8 0 – 5.171 0 – 0.25 -22 - 16 dB value range (Maximum 0.0001- 6.496, 0.0001-0.420 0.0001 – 2.879 0.0001- 1.254 (Deep) 0.0001 – 3.57 0.0001 – 0.0188 -22 - -1.11102 inundation extent (Deep) (Non-Flooded) (Deep) 1.254 – 1.778 (Deep) (Non-Flooded) (Non- Flooded) delineated from 6.496 - 11.604 0.420 – 0.533 2.879 – 5.699 (Shallow) 3.57 – 4.267 0.0188 – 0.0447 -1.11102 – 0.0426 (Shallow) Visual (Shallow) (Shallow) (Shallow) 1.778 – 8 (Shallow) (Shallow) -0.0426 - -0.0001 Interpretation) 11.604 – 190 0.533 – 0.80 5.699 – 190 ( Non-Flooded) 4.267 – 5.171 0.0447 – 0.25 (Deep) (Non-Flooded) (Deep) (Non-Flooded) i.e. dB <1.778 (Non-Flooded) (Deep) 0.0001 – 16 i.e. dB<11.604 i.e. dB>0.420 i.e. dB<5.699 i.e. dB<4.267 i.e. dB> 0.0188 (Non-Flooded) i.e. dB range -1.11102 - -0.0001

## Threshold ranges corresponds from that of Multi-temporal RADARSAT imagery, (dB,50 m). Same threshold ranges applied to DN,50m;DN, 100m for Textural classification of Flooded and Non-Flooded).

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Table 5-23 Multi- temporal RADARSAT ( 50 m,dB value ) -Textural Analysis for inundation extent

Extent of study area: Multi- temporal RADARSAT (50 m, dB value) -Textural Analysis for inundation extent 1160.08 Square Km.

Date of Acquisition Variance Homogeneity Contrast Dissimilarity Entropy Second moment Correlation (inundation extent in (inundation extent (inundation extent in (inundation extent in (inundation extent in (inundation extent in Sq.Km) (inundation extent in Sq.Km) Sq.Km) in Sq.Km) Sq.Km) Sq.Km) Sq.Km)

20.09.2003 (Minimum inundation extent delineated from 479.175 447.48 405.85 408.02 413.23 413.82 441.12 Visual Interpretation) (14.63 %) (7.052 %) (-2.906 %) (-2.38 %) (-1.141 %) ( -1.00 %) (5.53 %)

418.005 Sq.Km 11.09.2003 (Maximum inundation extent 568.04 586.52 564.22 560.84 600.38 584.21 512.91 delineated from Visual (-2.67 %) (0.498 %) (-3.32 %) (-3.90 %) (2.87 %) (0.102 %) (-12.11 %) Interpretation)

583.61 Sq.Km Variation in inundation extent by applying threshold corresponds to Max. & Min. inundation extent observed from Visual interpretation (in Sq.Km)

4-09-03 Min. 459.44 441.16 634.72 457.32 408.07 430.76 647.96 (-7.42 %) ( -11.10%) ( 27.89 %) ( -7.85 %) ( -17.77 %) ( -13.202%) ( 30.56%) 496.28 Max. 307.05 220.15 602.09 654.92 443.96 117.44 501.66 Sq.Km (-38.12%) (-55.63%) (21.32%) (31.96%) (-10.54%) (-76.33%) (1.084%) 13-09-03 Min. 410.24 489.09 288.92 130.32 364.33 202.29 490.97 (-9.00%) (8.47%) (-35.91%) (-71.09%) (-19.19%) (-55.13%) (8.89%)

450.86 Max. 409.60 397.82 388.74 284.53 400.86 412.11 377.38 Sq.Km (-9.15%) (-11.76%) (-13.77%) (-36.89%) (-11.08%) (-8.59%) (-16.29%)

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Table 5-24 Multi- temporal RADARSAT ( 50 m,DN value ) -Textural Analysis for inundation extent

Extent of study area: 1160.08 Square Multi- temporal RADARSAT ( 50 m,DN value ) -Textural Analysis for inundation extent Km.

Date of Acquisition Variance Homogeneity Contrast Dissimilarity Entropy Second moment Correlation (inundation extent in (inundation extent in (inundation extent in (inundation extent in (inundation extent in (inundation extent in Sq.Km) Sq.Km) Sq.Km) Sq.Km) Sq.Km) Sq.Km) (inundation extent in Sq.Km)

20.09.2003

(Minimum inundation extent delineated 395.70 424.81 413.22 408.02 138.474 413.82 from Visual Interpretation) (-5.33%) (1.629 %) (-1.143 %) (-2.38 %) (-66.87%) ( -1.00 %) 378.38 418.005 Sq.Km (-9.478 %) 11.09.2003

(Maximum 568.052 587.265 564.66 561.45 563.955 584.78 (-2.66%) (0.626 %) (-3.24 %) (-3.79 %) (-3.367 %) (0.201%) inundation extent delineated from 350.44 583.61Sq.Km (-39.95 %) Variation in inundation extent by applying threshold correspond to Max. & Min. inundation extent observed from Visual interpretation (in Sq.Km)

4-09-03 Min. 579.047 534.137 353.79 207.96 397.18 144.217 496.75 (16.67 %) ( 7.628%) ( -28.711 %) ( -58.09 %) ( -19.96 %) ( -70.94%) ( 0 094%) 496.28 Sq.Km Max. 240.41 325.09 112.97 188.12 254.62 287.46 367.83 (-51.55%) (-34.49%) (-77.23%) (-62.09%) (-48.69%) (-42.07%) (-25 88%) 13-09-03 Min. 347.11 536.36 628.51 409.935 532.072 774.5 390.88 (-23.01%) (18.96%) (39.40%) (-9.07%) (18.012%) (71.78%) (-13 303%) 450.86 Sq.Km Max. 327.60 397.46 310.98 334.59 400.82 412.367 215.15 (-27.33%) (-11.84%) (-31.025%) (-25.786%) (-11.097%) (-8.53%) ( 52 27%)

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Table 5-25 Multi- temporal RADARSAT ( 100 m,DN value ) -Textural Analysis for inundation extent

Extent of study area: Multi- temporal RADARSAT ( 100 m,DN value ) -Textural Analysis for inundation extent 1160.08 Square Km.

Date of Acquisition Variance Homogeneity Contrast Dissimilarity Entropy Second moment Correlation (inundation extent (inundation extent in (inundation extent in Sq.Km) (inundation extent in Sq.Km) (inundation extent in (inundation extent in (inundation extent in in Sq.Km) Sq.Km) Sq.Km) Sq.Km) Sq.Km)

20.09.2003 (Minimum inundation extent delineated from 478.97 291.32 483.46 404.30 346.08 264.78 444.42 Visual Interpretation) (14.58 %) (-30.30 %) (15.66 %) (-3.27 %) (-17.205%) ( -36.65 %) (6.32%)

418.005 Sq.Km 11.09.2003 (Maximum inundation extent 278.96 379.89 295.08 531.52 389.18 428.56 281.10 delineated from Visual (-52.20%) (-34.906 %) (-49.43 %) (-8.925 %) (-33.315%) (-26.56%) (-51.834 %) Interpretation)

583.61Sq.Km

Variation in inundation extent by applying threshold corresponds to Max. & Min. inundation extent observed from Visual interpretation (in Sq.Km)

4-09-03 Min. 530.35 461.95 405.78 295.90 411.40 381.29 566.79 (6.86 %) ( -1.476%) ( -18.235 %) ( -40.37 %) ( -17.103 %) ( -23.17%) (14.207%)

496.28 Max. 375.01 676.09 203.53 320.17 639.20 686.29 567.94 Sq.Km (-24.43%) (36.23%) (-58.98%) (-35.48%) (28.79%) (38.28%) (14.439%) 13-09-03 Min. 476.08 251.75 343.34 338.11 249.09 500.73 405.26 (5.59%) (-44.16%) (-23.84%) (-25.00%) (-44.75%) (11.061%) (-10.114%) 450.86 Max. 493.64 540.63 256.64 296.56 509.71 556.75 405.26 Sq.Km (9.48%) (19.91%) (-43.07%) (-34.22%) (13.052%) (23.48%) (-10.11%)

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5.3. GIS Approach Analysis

5.3.1.1. Generation of Cost-distance raster

Devastating flood is generally low frequency, high magnitude natural phenomenon. In almost all flooding situations, peak stage remains only for a couple of hours, but in most of the case the most extensive and severe damage takes place during that time (Sanyal, 2003). With the current temporal resolution of various satellite images, it's very difficult to capture the spatial extent of flood at its peak. Thus, an attempt was made to capture the maximum inundation extent corresponding to the ground peak flooding stage by integration of satellite data with GIS technique. The attempt were made to generate a cost-distance map where it was used to calculates the least- accumulative-cost distance over the surface of the study area to a set of source points which was identified near the embankment breach section of the main Daya River which inturn produces a direction surface for each cell to its closest source point.

It was found that the ‘least accumulation cost distance’ operates on certain values of the raster cells which represent the roughness of the terrain causing frictional drag to the overflowing flood water (Sanyal,2003). Since roughness of the terrain is a function of a host of other geomorphic and litho logical factors, it is very difficult to control this parameters in area's like Orissa where there is diverse in Landuse and geomorphological characteristics. Moreover, for generation of a cost distance raster, a higher precision and vertical accuracy elevation data is needed. Since, the topography of the study area is flat i.e. the elevation difference is about 5 m to 15 m, using an elevation dataset like ASTER-DEM and SRTM DEM does not suffice the need to generate an accurate Cost-distance raster. In addition, using field contour 1 metre map generated elevation data could not suffice for this work due to the inherent error and inaccuracy of the obtained field contour map. In general, due to unavailability of an accurate DEM to be incorporated in the cost-distance function, the result obtained using the available DEM does not gives satisfactory results which can be used for further processing to extract the maximum inundated extent as discussed in pervious chapter.

5.3.2. Extraction of Maximum inundated extent

As discussed in above section, due to unavaliabilty and non-suitability of the DEM used, extraction of maximum inundation extent could not be accomplished.

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6. Conclusion and Recommendation

6.1. Conclusions

The conclusions of this study are given here with respect to each of the research questions framed in beginning of the thesis.

Is it possible to obtain the Flood’s maximum extent from multi-source and multi- temporal satellite imagery?

Deriving the maximum flood inundation extent was achieved through remote sensing approach within the limitation of the temporal satellite data availability. Yes, it is possible to extract the maximum relative flood extent using remotely sensed multi-sensor and multi-date dataset by processing, analyzing and comparing, using various digital classification techniques like textural based classification, thresholding, Principal component analysis considering flood extent from visual interpretation as a reference extent in context of remote sensing approach point of view and extrapolation of these results to other dates. On the other hand, due to non-availability and inaccuracy of the DEM used, generation of cost-distance grid (least accumulation cost-distance matrix) did not give satisfactory results for generating a maximum flood extent map from GIS based approach. Hence, there is a necessity of accurate vertical DEM/elevation data to be incorporated to make a cost-distance grid which inturn will give a maximum extent map. Here comparison between remote sensing approach derived extents could not be compared with that of GIS approach outputs.

What is the variation in flooded spatial extent using various satellite data by remote sensing & GIS approach?

Variation in Flooded spatial extent are analysed from the available dataset that are used for this study. The variation in extent within optical dataset having different spatial resolution but acquired on same date 08-9-2003 i.e. LISS-III(23.5 m),IRS-Pan (5.6 m), Pan sharpened LISS (5.6m) were analysed and it was seen that the variation in extent between Pan and LISS-III is almost same having 49.57 % and 49.75% inundation respectively. On the contrary, when Pan is compared with Pan sharpened LISS; there is 0.89 % increase inundation extent in Pan Image. Likewise, comparison within the various RADARSAT images were done and it was seen that there is a large variation in extent when dB, 50 m were compared with DN, 100m. Spatial extent could also be seen when dB, 50 m is compared with DN, 50 m. Moreover, comparing the extent obtained by applying various digital techniques was also performed and it was verified that thresholding techniques gives a stable extent across all the three RADARSAT data. It’s seen that trend of flood extent from 4th to 20th is same and highest extent is observed in September 11th 2003 having percentage inundation of 46.43 %, 48.85% and 56.03 % for

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dB, 50m; DN, 50 m and DN, 100 m respectively. Similarly, the least extent is observed on 20th September having percentage extent of 45.81 %, 35.66 %, and 43.18 % in three same dataset.The thresholding extent obtained from dB, 50 m was quite reasonable when compared with that of visual interpretation extent as the distribution range of dB value within the flooded/inundated region is quite large. When comparing the extent obtained from various textural images, it was observed that there is a large variation in all the three dataset. The reason behind it is due to the inherent characteristic of each texture images generated and it depends on the range of texture value for each texture images. Large variation in extent were observed in Homogeneity, Contrast and Second Moment textural measures when compared with other measures like correlation, variance etc. a variation in spatial extent is observed when thresholding technique is applied to Original RADARSAT and to that of corresponding textural measures images. Homogeneity measures gives 50.62 % inundation extent as compared to 48.85% extent from original image i.e. a variation of 1.77% could be observed. Above all, from this study we could conclude that depending upon the techniques and the dataset used, variation in extent could be captured thereby helping to judge the accurate extent for further analysis. Extent from GIS approach couldn’t be achieved due to constraint in obtaining the right kind of DEM for processing and analysis the GIS section.

Can additional topographic, historic and geomorphologic data improve the accuracy of the flood extent assessment?

Incorporating topographic, historic and geomorphologic data for a detailed study related to Flooding and its inundation problem is an essential and necessary criterion, which helps in proper assessment and analysis of the situation. Moreover, using such records and data, it would help us to analyse different phenomenon under consideration in term of their temporal dimensions and extreme expression. In this study only Historic and Geomorphic data are analysed to understand the accuracy of the flood extent. In short, the analysis of the historic and geomorphic data allowed us to study the behaviour of the Daya River and its flooding problem. Topographic data could not be obtained due to sensitivity of the region and its unavailability. In absence of topographic data, geomorphic data gives the clear picture of the flooding pattern that was within the period under consideration for this study.

Can we obtain an impression of the dynamics of the Daya River, based on written historical records & available hydrological and geomorphologic data?

Analysis of written historical data’s, record and geomorphologic data give an insight to understand the dynamic of Daya Flooding event 2003. Flood frequency analysis of past 40 years peak flood discharge data, determination the flooding trend for the past 40 years improve and add to understand the flooding situation more clearly at this region. Available hydrological data (maximum peak discharge for 40 years) from 1964 to 2003, allowed us to calculate the return periods and its exceedence probability. From this it was verified that 2003 flood event is the third highest flood within this 40 year time-period of flood occurrence in and around Daya system. The highest flood occurred in the year 1982 with a water height of

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28.52 m with a maximum discharge of 44750 cumec and has return period of 41. In case of 2003 flood event, the recorded highest flood height was 23.45 m having a return period of 1.03. On the other hand, analysis of various geomorphic units present with that of Flood evolution map generated through remote sensing approach gives the dynamic of each geomorphic units and the pattern of inundation extent. Hence, the potential and constraint of flooding zone in and around Daya river system could be clearly identified and analysed, thereby helping us to understand the dynamic nature of Daya and its influence in its surrounding areas.

6.2. Limitation of the Research

Availability of the right kind of dataset at the right time is considered as the most serious issues in this context. There were many limitations in this research pertaining to this study but availability of dataset is one of the major issues. There is a constraint in availability of time-series temporal dataset for the whole flooding period. Only 4 temporal dataset have been used in a period of 23 days i.e. 28th August 2003 to 20th September 2003. In addition, non-availability of pre-flood RADARSAT imagery for a complete detailed analysis and comparison within the Microwave images. More cloud free data in the visible band i.e. optical images would help in synergizing with that of Microwave- RADARSAT data’s. Another main important issues and limitation is the availability of accurate DEM. Accurate DEM would have help in generating the maximum flood extent map for this study.

There is a need to develop and used advanced algorithm like EBIS (Evidence based interpretation of satellite image) for accurate classification of SAR imagery.

6.3. Recommendation

Automatic methods of flood mapping are quite useful for site specific studies comparing with time consuming and accurate visual interpretation. More multi-temporal and time series dataset should be incorporated for this type of study to understand the real dynamic and the pattern of inundation. Advanced techniques like Independent component analysis (ICA) and Wavelet based transformation should be attempted to such multi-temporal dataset for identifications of spatio-temporal variability in multivariate data that will inturn help to capture the variability arising from time series images i.e. the spatial and temporal pattern of flooding.

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Appendix:

Gauge Reading at Selected location for 2003 Flood Event:

Measured water level at three gauge stations along the Daya River is given in tabular form below. This has been obtained from the Flood cell, Bhubaneshwar. There is no gauge reading available for other four stations i.e. Ghoradia, Balabhadrapur, Nuagoan, Basantpur all at the lower catchments of Daya during 2003 Flood event: Table: I Water level at Madhipur Gauging site

Station Geographic location Zero Danger Height/Depth Date of Measurement (10.00Hrs) Measured Water Water Height/Excess Name (Lat/Long) Level(Z.L.)Mt. Level(D.L.) of water from Level(M.W.L) Mt. water (causing Mt. Z.L (D.L. -Z.L) inundation/flooding) Mt. Madhipur 20°−7'-18"N / 85º-48´-6˝E 7.535 11.3 3.765 30th August 2003 12.055 0.755 11.3 31st August 2003 12.055 0.755 11.3 1st September 2003 12.055 0.755 11.3 2nd September 2003 12.185 0.885

11.3 3rd September 2003 11.795 0.495 11.3 4th September 2003 11.825 0.525 11.3 5th September 2003 11.915 0.615 11.3 6th September 2003 11.665 0.365 11.3 7th September 2003 11.475 0.175 11.3 8th September 2003 11.725 0.425 11.3 9th September 2003 11.755 0.455 11.3 10th September 2003 11.84 0.54 11.3 11th September 2003 11.57 0.27 11.3 12th September 2003 11.035 -0.265

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Table: II Water level at Kanas Gauging site

Station Geographic Zero Level(Z.L.)Mt. Danger Height/Depth Date of Measurement Measured Water Height/Excess water Name location Level(D.L.)Mt. of water (10.00Hrs) Water (causing inundation/flooding) (Lat/Long) from Z.L Level(M.W.L) (D.L. -Z.L) Mt. Mt. Kanas 20°−6'- 0.76 4.75 3.99 27th August 2003 2.5 -2.25 27"N/85°−38'- 27"E 4.75 28th August 2003 3.17 -1.58 4.75 29th August 2003 3.6 -1.15 4.75 30th August 2003 4.39 -0.36 4.75 31st August 2003 3.48 -1.27 4.75 1st September 2003 4.23 ---- 4.75 2nd September 2003 5.03 0.28 4.75 3rd September 2003 5.03 0.28 4.75 4th September 2003 4.97 0.22 4.75 5th September 2003 4.98 0.23 4.75 6th September 2003 5.1 0.35 4.75 7th September 2003 5 0.25 4.75 8th September 2003 4.89 0.14 4.75 9th September 2003 4.85 0.1 4.75 10th September 2003 4.85 0.1 4.75 11th September 2003 4.86 0.11 4.75 12th September 2003 4.76 0.01 4.75 13th September 2003 4.56 -0.19 4.75 14th September 2003 4.26 -0.49 4.75 15th September 2003 3.97 -0.78 4.75 16th September 2003 3.84 -0.91 4.75 17th September 2003 3.84 -0.91 4.75 18th September 2003 3.86 -0.89 4.75 19th September 2003 3.66 -1.09 4.75 20th September 2003 3.32 -1.43 4.75 21st September 2003 3.06 -1.69 4.75 22nd September 2003 2.85 -1.9 4.75 23rd September 2003 2.68 -2.07 475 118 24th September 2003 244 231 RECONSTRUCTION OF 2003 DAYA RIVER FLOOD, USING MULTI-RESOLUTION AND MULTI-TEMPORAL SATELLITE IMAGERY

Table: III Water level at Kanti Gauging site

Station Geographic Zero Danger Height/Depth Date of Measurement (10.00Hrs) Measured Water Water Height/Excess water Name location Level(Z.L.)Mt. Level(D.L.)Mt. of water from Level(M.W.L) Mt. (causing (Lat/Long) Z.L (D.L. - inundation/flooding) Z.L)Mt. Kanti 20°−8'-6"N/ 85°- 46'-9"E 5.855 9.62 3.765 30th August 2003 (09.00Hrs, 10.00Hrs) 11.785,10.355 2.165,0.735 9.62 2nd September 2003(06.00Hrs, 1500 Hrs) 11.125, 11.095 1.505,1.475 9.62 3rd September 2003(10.30Hrs,13.00Hrs) 10.885,10.845 1.265,1.225 9.62 4th September 2003( 00.00Hrs,20.00Hrs) 10.795,10.675 1.175,1.055 9.62 5th September 2003 10.775 1.155 9.62 6th September 2003 10.715 1.095 9.62 7th September 2003 10.555 0.935 9.62 8th September 2003 10.615 0.995 9.62 9th September 2003 10.675 1.055 9.62 10th September 2003 10.78 1.16 9.62 11th September 2003 10.64 1.02 9.62 12th September 2003(10.00HRS, 2300HRS) 10.275,9.985 0.655,0.365 9.62 13th September 2003 9.615 -0.005

## The zero level and Danger level specified by the Flood cell for the other four stations i.e. Ghoradia, Balabhadrapur, Nuagoan, Basantpur are as follows:

Station Name Zero Danger Level(Z.L.)Mt. Level(D.L.)Mt. Ghoradia 4.56 6.24 Balabhadrapur 1.45 3.3 Nuagoan 0 1.85 Basantpur N.A. N.A.

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