Reconstruction of the 2003 Daya River 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, INDIA & 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 Mahanadi 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, Puri; 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 (Puri district,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- Mahanadi river delta...... 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: