ABSTRACTDue to the penetration capacity of Synthetic Aperture Radar (SAR) data through clouds and hazy atmospheric circumstances like fog, smog, light rain, mist etc., it has ability to continuous observe flood events for producing accurate, rapid and cost effective flood mapping. Therefore, microwave Sentinel-1A data for monsoon period of year 2017 has been used for flood inundation mapping for Darbhanga and Samastipur districts of Bihar, India. SNAP software (Sentinels Application Platform) was used to pre-process the SAR imagery. The SAR imagery was first subset according to the study area then calibrated, geometrically corrected and filtered. Afterward the threshold method was applied to extract the inundated area from SAR imagery by using density slicing technique for separating the water from non-water class. For delineation of flooded area the permanent water bodies (like rivers, ponds, lakes etc.) were subtracted from the water class extracted from LULC map. Then the flood map was superimposed and analysed to find out the nature of spatial extent, duration of flood and to show how flooding spread through time. Flood inundation layer was used to find the flooded area of LULC classes, particularly focussing on the agricultural land and infrastructure (roads and railways), damage assessment has been carried out. This study illustrates that how SAR data along with GIS can be used effectively for flood mapping, monitoring and damage assessment (agricultural & infrastructural damage). The results or findings of this study will help to plan relief efforts and hence in the process of flood management.
Keywords: Flood Inundation Mapping, Radar altimetry, SAR imagery, Density slicing, Flood depthINTRODUCTION2.1Definition of FloodFlood comes under one of the most costly and frequent natural disasters.
National Institute of Disaster Management (NIDM) defines flood as “an excess of water (or mud) on land that’s normally dry and is a situation where in the inundation is caused by high flow, or overflow of water in an established watercourse, such as a river, stream, or drainage ditch; or ponding of water at or near the point where the rain fell. This is a duration type event. A flood can strike anywhere without warning, occurs when a large volume of rain falls within a short time.” CITATION NID l 16393 (Kanda & Aggarwal, 2008)2.2 Flood Mapping and MonitoringTraditional methods of flood mapping are based on ground surveys and aerial observations, but when the phenomenon is widespread, such methods are time consuming and expensive. Furthermore timely aerial observations can be impossible due to prohibitive weather conditions. An alternative option is offered by satellite remote sensing technology. Now for this optical data acquired by sensors on board spacecraft have been used to map inundated areas CITATION PAB02 l 16393 (P.A. Brivio, 2002). Flood monitoring constitutes identification of inundated areas and estimation of flood water depth. Flood inundated areas can be identified using flood mapping techniques which will give flood extent of the study area. Time series analysis can be done for showing the changes in the inundated areas and changes in flood water depth over the time.
2.3 Flood Water Level EstimationThere are two methods for estimating Flood water level:-
Estimating flood water level using DEM – Flood water depth can be calculated using flood extent map and DEM. The flood extent map will have several polygons of flood water. The boundary of flood polygon will have high elevation and the inner portion of the polygon will have low elevation. The difference of two elevations will give the flood depth.
Estimating flood water level using Altimetry data –Radar altimetry, a profiling technique, measures the two-way travelling time of a pulse emitted by the antenna onboard a satellite and its reflection from the earth’s surface. The repeated along-track nadir measurements are used for calculation of water stage time series over water bodies CITATION Lee01 l 16393 (Lueng & Cazenave, 2001). For studying changes in water level altimeter data is very useful. It can also be used to estimate changes in the flood water levels over the period of time.
2.4 Study AreaBihar is one of the most disaster prone states of the country. Disaster affecting the state includes Floods, Droughts, Earthquakes, Heat/Cold waves, etc. Flood is the most prevalent phenomenon in Bihar. We have chosen two districts Darbhanga and Samastipur of the state as our study area. These two districts face flood almost every year due to Kosi River and Ganga River.
Fig. 2.1 Location Map of the Study Area2.4.1 LocationDarbhanga district is one of the thirty-seven districts of Bihar state with Darbhanga town as its administrative head-quarter. It is situated between longitude 85° 45′ – 86° 25′ East and latitude 25° 53′ – 26° 27′ North Total geographical area of the district is 2,279 km². CITATION Shr13 l 16393 (Sahu, 2013)Samastipur district is one of the districts of North Bihar. The District of Samastipur was carved out of the erstwhile District of Darbhanga on 14 November 1972. Samastipur district is spread over an area of 2624.82 km².CITATION Sri13 l 16393 (Shukla, 2013)2.4.2 DrainagePhysiographically the district Darbhanga forms a vast monotonously flat plain with low relief to the south of Tarai/Bhabhar high relief belt in Nepal Himalayas. The area is a part of the Bagmati Sub-Basin in the Ganga Basin.
The district contains four main river systems, viz. the Bagmati, little Bagmati, Kamla and Tiljuga. CITATION Shr13 l 16393 (Sahu, 2013)The Samastipur district is a part of Ganga basin. Ganga river skirts the district on the south and flows towards east. Rivers like Burhi Gandak, Bagmati, Baya, Kamla, Kareh, Nun and Jhamwari and Balan traverse Samastipur district. However, the Burhi Gandak and the Ganga constitute the principal drainage in the area.CITATION Sri13 l 16393 (Shukla, 2013)
Fig. 2.2 Drainage System of the Study Area Source: District Census Handbook, 2011
2.4.3 Geomorphology2.4.3(a) Darbhanga
The district has a vast alluvial plain devoid of any hills. There is a gentle slope from north to south with a depression on the centre. The maximum ground elevation is 52.50 m masl in northern part of the district and the minimum is 41.08 m masl in the south- eastern parts, average being 47 m masl. Levees along the stream banks, back swamps or flood basins/ chaurs of various sizes are the only significant features over the area.
The District of Darbhanga can be divided into four natural divisions:-
The eastern portion consisting of Ghanshyampur, Biraul and Kusheshwarsthan blocks contain fresh silt deposited by the Kosi River. This region was under the influence of Kosi floods till the construction of Kosi embankment in the Second Five Year Plan. It contains large tracts of sandy land covered with wild marsh.
This division comprised of the anchals lying south of the Burhi Gandak river and is the most fertile area in the district. It is also on higher level than the other part of the district and contains very few marshes. It is well suited to the Rabi crops.
The third natural region is the doab between the Burhi Gandak and Baghmati and consists of the low-lying areas dotted over by chaur and marshes. It gets floods every year.
The fourth division covers the Sadar sub-division of the district. This tract is watered by numerous streams and contains some up-lands.
CITATION Shr13 l 16393 (Sahu, 2013)2.4.3(b) Samastipur
The district is flat without any elevated land to break the monotony of area. However, levees, small mounds and shallow depressions or the chaurs are only relief observed in the area which is permanently water logged.
The general elevation of the land surface varies from 40-42mamsl.The plain of the area is characterized by thick pile of alluvial deposits with varying depth and formed by aggregation of alluvial fans of river Burhi Gandak and Bagmati.
Based mainly on the depositional /erosional history, extent of oxidation and pedogenic character, the relative proneness to annual flooding and land use practice of the study area have been classified into two major geomorphic units.
Kamla Surface or the present flood plain: It is It is equivalent to “Diara unit”
Jaynagar Surface: It is older flood plain and is equivalent to “Vaishali” surface of the Gandak basin.CITATION Sri13 l 16393 (Shukla, 2013)2.4.4 Climate and RainfallThe Darbhanga district has dry climate whereas Samastipur district lies in tropical zone characterized by Semi-arid to Sub-tropical climate. There are three well-marked seasons, the winter, the summer & the rainy season.
Winter: The winter season commences in November and continues up to February, though March is also somewhat cool. For Samastipur January is the coolest month with minimum temperature between 7-10° C and maximum temperature in the range of 20-25° C.
Summer: In Darbhanga Westerly winds begin to blow in the second half of March and temperature rises considerably. Summer season starts from March and lasts till June in Samastipur District. May is the hottest month In Darbhanga district temperature rises upto 42° C and the maximum temperature varies from 21.2° C to 36.5° C in Samastipur district.
Monsoon: Rain sets in towards the middle of June. With the advent of the Rainy seasons, temperature falls and humidity rises. The moist heat of the Rainy season is very oppressive up to August .The rain continues till the middle of October. Average rainfall is 1142.3 mm. around 92% of rainfall is received during monsoon period.CITATION Shr13 l 16393 (Sahu, 2013)CITATION Sri13 l 16393 (Shukla, 2013)2.4.6 Land Use and Land CoverAgriculture is the major land use class of the study area, around 81% of the area is covered by the agricultural land. It is also one major class which is affected by the flood during monsoon season. Followed by Wasteland as second major class, Built-up and Forest class covers approx. 2% of the study area each. Water body constitutes 1% of the study area.
2.4.7 InfrastructureDarbhanga district has two National Highways passing through it NH 57 and NH 105.Two Railway lines also crosses the district one from Samastipur to Nirmali another from Pupri which joins the railway line from Samastipur in the district.
Samastipur district also has NH103, NH 28 and SH49. Railway line from Muzaffarpur to Khagaria, Darbhanga and Barauni. Another line from Hajipur to Bachhwara.
3. LITERATURE REVIEW3.1 Definition of Flood?Flood comes under one of the most costly and frequent natural disasters.
According to United Nations Office for Outer Space Affairs:-
“Flood is a general and temporary condition of partial or complete inundation of normally dry land areas from overflow of inland or tidal waters from the unusual and rapid accumulation or runoff of surface waters from any source.”
3.2 Types of Flood
Flash Floods: Floods occurring within six hours, mainly due to heavy rainfall associated with towering cumulus clouds, thunderstorms and tropical cyclones or during passage of cold weather fronts, or by dam failure or other river obstruction. This type of flood requires a rapid localized warning system.
Riverine/Fluvial Floods: Flood that occurs when excessive rainfall over an extended period of time causing a river to exceed its capacity and subsequently flows onto land.
Coastal Floods: Floods associated with cyclonic activities like Hurricanes, Tropical cyclones, etc. generating a catastrophic flood from rainwater which often aggravate wind-induced storm and water surges along the coast.
Urban Flood: As land is converted from agricultural fields or woodlands to roads and parking lots, it loses its ability to absorb rainfall. Urbanization decreases the ability to absorb water 2 to 6 times over what would occur on the natural terrain. During periods of urban flooding, streets can become swift moving rivers, while basements can become death traps as they fill with water.
Ice Jam: Floating ice can accumulate at a natural or human-made obstruction and stop the flow of water thereby causing floods. Flooding too can occur when there the snow melts at a very faster rate.
Glacial Lake Outbursts Flood (GLOF): Due to faster rate of ice and snow melting, possibly caused by the global warming, the accumulation of water in these lakes has been increasing rapidly and resulting sudden discharge of large volumes of water and debris and causing flooding in the downstream.CITATION NID l 16393 (Kanda & Aggarwal, 2008)3.3 Causes of FloodInadequate capacity of the rivers to contain within their banks the high flows brought down from the upper catchment areas following heavy rainfall, leads to flooding. The tendency to occupy the flood plains has been a serious concern over the years. Because of the varying rainfall distribution, areas which are not traditionally prone to floods also experience severe inundation.
Areas with poor drainage facilities get flooded by accumulation of water from heavy rainfall. Excess irrigation water applied to command areas and an increase in ground water levels due to seepage from canals and irrigated fields also are factors that accentuate the problem of water-logging.
The problem is intensified by factors such as
Silting of the riverbeds
Reduction in the carrying capacity of river channels
Erosion of beds and banks leading to changes in river courses
Synchronization of floods in the main and tributary rivers and retardation due to tidal effects.
Excessive rainfall in river catchments or concentration of runoff from the tributaries and river carrying flows in excess of their capacities.
Cyclone and very intense rainfall when the EL-Nino effect is on a decline.
Poor natural drainage system.CITATION NID l 16393 (Kanda & Aggarwal, 2008)3.4 Remote Sensing as a Technology for Flood Mapping and Monitoring For formulating any flood management strategy the first step is to identify the area most vulnerable to flooding, with the equipment currently installed at river gauging stations it is sometimes difficult to record an extreme flood event having a very high return period. Remote sensing is a reliable way of providing synoptic coverage over a wide area in a very cost effective manner. It also overcomes the limitation of the ground stations to register data in an extreme hydrological event. In addition multi-date imageries equip the investigators with an additional tool of monitoring the change or reconstruct progress of a past flood.
Floods can be mapped and monitored with remotely sensed data acquired by aircraft and satellites, or even from ground-based platforms. The sensors and data processing techniques that exist to derive information about floods are numerous. Instruments that record flood events may operate in the visible, thermal and microwave range of the electromagnetic spectrum.
It is evident that GIS has a great role to play in natural hazard management because natural hazards are multi-dimensional and the spatial component is inherent. The main advantage of using GIS for flood management is that it not only generates a visualization of flooding but also creates potential to further analyze this product to estimate probable damage due to flood.
Smith reviews the application of remote sensing for detecting river inundation, stage and discharge. Since then, the focus in this direction is shifting from flood boundary delineation to risk and damage assessment.
Apart from providing direct information about flooding, remote sensing data can also be integrated with flood models (via model calibration or validation, and data assimilation techniques) or provide floodplain topography data to augment the amount and type of information available for efficient flood management. CITATION Smi97 l 16393 (Smith, 1997)Remote Sensing In Damage Assessment:- Erosion of top soil due to a flash flood and sediment deposition over the course of stream reduces the fertility of soil and thus have a negative impact on agricultural economy. The process of change detection is found useful to monitor this kind of damage to agricultural land. The most widely used procedure is to monitor the change in brightness value (VB) at a particular wavelength or different bands to identify the erosion caused by a floodCITATION Joy04 l 16393 (Lu, et al., 2004). Damage assessment can also be done using flood extent/inundation map of the area. The flood extent map can be overlaid on different commodities like infrastructure (road, railways, etc.), administrative boundaries, LULC. Agricultural damage assessment is another type which will show all the agricultural fields inundated with flood water and if data of agricultural land with their owners name is available it can be used during the rehabilitation period. Government can use the data for distribution of compensation money to the owners of inundated agricultural fields.
3.5 Advantages of Microwave over Optical Remote Sensing for Flood MappingThe existence of cloud cover appears as the single most important impediment to capture the progress of floods in bad weather condition. The development of microwave remote sensing, particularly radar imageries, solve the problem because the radar pulse can penetrate cloud cover. Currently the most common approach to flood management is to use synthetic aperture radar (SAR) imagery and optical remote sensing imagery simultaneously in one project. Apart from its all-weather capability the most important advantage of using SAR imagery lies in its ability to sharply distinguish between land and water. Thresholding is one of the most frequently used techniques in active Remote Sensing to segregate flooded areas from non-flooded areas in a radar image. Commonly, a threshold value of radar back scatter is set in decibel (dB) and a binary algorithm is followed to determine whether a given raster cell is ‘flooded’ or not. Radar back scatter is computed as a function of the incidence angle of the sensor and digital number (DN). The threshold values are determined by a number of processes depending on the study area and overall spectral signature of the imagery. Change detection can be used as a powerful tool to detect flooded area in SAR imageryCITATION Joy04 l 16393 (Lu, et al., 2004).
3.5.1 Polarization of SAR signalThe polarization of a SAR instrument refers to the orientation of the transmitted SAR beam’s electric field vector. In case of the vector oscillating in the horizontal direction, the beam is said to be “H” polarized and perpendicular to the horizontal direction, the beam is known as “V” polarized. 4 possible polarization combinations are HH, VV, HV & VH. SAR polarization is a key factor in flood detection. HH-polarized images are considered more adequate for flood detection than VV- or cross-polarized images. This is mostly due to the fact that HH- polarization gives the highest distinction in backscatter values between dry and wet forested areasCITATION Emm17 l 16393 (Psomiadis & Emmanouil, 2017)3.6 Flood Mapping and Damage Assessment Flood Hazard Mapping is an exercise to define those areas which are at risk of flooding under extreme conditions. As such, its primary objective is to reduce the impact of flooding. “Flood Hazard Mapping is a vital component for appropriate land use planning in flood-prone areas. It creates easily-read, rapidly-accessible charts and maps which facilitate the identification of areas at risk of flooding and also helps prioritize mitigation and response efforts”CITATION GVB05 l 16393 (Bapulu & Sinha, 2005).
3.6.1 Mapping Flood Extents Using Sentinel 1A DataMapping flooded areas can be a very useful and important undertaking after flooding has occurred. Not only can it help to identify flooded areas, which can aid emergency response, it can also assist with long-term planning and defence against flooding in the future. The EU’s Copernicus programme includes various satellite constellations for monitoring different environmental features across the globe. The Sentinel-1 and Sentinel-2 constellations are especially useful for monitoring and assessing environmental variables at medium resolutions. Both Sentinel-1 and Sentinel-2 constellations are comprised of two satellites. The Sentinel-1 satellites (1A and 1B) are Synthetic Aperture Radar (SAR) platforms, while the Sentinel-2 satellites (2A and 2B) are multispectral optical imaging platforms. Further all the data is open source and is thus freely available. When a radar pulse encounters an object, it is scattered in various directions depending on the texture and angles of the object. The radar sensor measures the backscatter of the signal. In general, the higher or brighter the backscatter on an image, the rougher the surface being measured. Flat surfaces reflect very little of the signal back towards the sensor and therefore tend to appear dark on an image.
In connection with natural hazards it can basically be distinguished between three categories of damage:
Direct flood damages result from the actions of floodwaters, inundation and flow, on property and structures.
Indirect damages arise from the disruptions to physical and economic activities caused by flooding. Indirect damages are for example: the loss of sales or the reduced productivity.
Tangible damages are monetary losses directly attributable to flooding. They may occur as direct or indirect flood damages.
Intangible damages arise from adverse social and environmental effects caused by flooding. Examples of intangible damages are: loss of life, emigration, stress and anxiety, damage caused to someone’s health etc. These intangible damages are not easily quantifiable and have not been included in the monetary assessment of flood damages.
Primary and secondary losses are distinguished in the sense that:
Primary damage result directly from the flood event
Secondary damage in the causation of the actual flood event distances themselves.CITATION Tag11 l 16393 (Mohamed & Gasmelsied, 2011) Advantages:-
Identification of those areas at risk of flooding will help inform emergency responses, location of flood shelters for evacuees, planning of more efficient emergency responses and infrastructure i.e. electricity supplies, sewage treatment, hospital, etc. should be located in low risk areas so that they can function during the flood event.
Flood hazard mapping will allow quantification of what is at risk of being flooded such as the number of houses or businesses. This will help identify the scale of emergency and clean-up operations.
The creation of flood hazard maps promotes greater awareness of the risk of flooding. This can be beneficial in encouraging hazard zone residents to prepare for the occurrence of flooding.
3.7 Satellite Altimetry The Satellite radar altimetry datasets are extensively used for continental water monitoring although it was primarily designed for oceanic surface and ice cap studies. Radar altimetry, a profiling technique, measures the two-way travelling time of a pulse emitted by the antenna onboard a satellite and its reflection from the earth’s surface. The repeated along-track nadir measurements are used for calculation of water stage time series over water bodies CITATION Fua06 l 16393 (Fua & Traon, 2006).Water level estimated from satellite altimetry can help to assess many hydrological parameters like river discharge and reservoir volume. These parameters can be employed for calibration and validation purposes of hydrological and hydrodynamic models, near real-time flood forecasting and many more. SARAL/AltiKa (Satellite with ARgos and AltiKa) have been successfully used for retrieving water level in reservoir and river, estimating river discharge and calculating reservoir sedimentation. The estimation of river water levels from altimeter requires number of range corrections due to atmospheric effects on microwave pulses. The various corrections are applied to account the time delay of microwave pulses such as dry tropospheric correction, wet tropospheric correction, ionospheric correction and correction for pole and solid tidal effects on the Earth. The presence of dry gases and water vapor in the troposphere and free electrons in the ionosphere affects microwave pulses. The dry tropospheric correction has the highest value as compared to other range and geophysical corrections. Microwave wavelengths are much sensitive to precipitation rather than dry gases, water vapor and clouds. The geophysical corrections and pole tide correction was due to deformation of the Earth induced by the satellite orbit altitude, Alt and the altimeter range, R measurement. Prior to obtaining the water surface elevation, atmospheric and geophysical corrections are applied on the retracked range values. Finally, orthometric height is obtained by subtracting geoid values with reference to WGS84 ellipsoid (eq. (1))
H=Alt – R – Dtc + Wtc + Ionc + Stc + Ptc – geoid (1)
Where H is the corrected orthometric height; Alt is the satellite altitude from reference ellipsoid; R is the satellite range; Dtc is dry tropospheric correction; Wtc is wet tropospheric correction; Ionc is ionospheric correction; Stc is the solid tide and Ptc is the pole tide correction. The elevation values are obtained by the eq. (1), H is considered with respect to the geoid.CITATION Sur17 l 16393 (Ghosh, et al., 2017)AIMS AND OBJECTIVESThe aim is to map and monitor flood using microwave data (Sentinel 1A SAR Datasets) for Darbhanga and Samastipur districts of Bihar for the year 2017.
The objectives of the present study are:
To map flood inundation using SAR data.
To assess damage using geospatial tools.
To find the flood depth using altimeter data and DEM.
MATERIALS AND METHODOLOGYThis chapter deals with the various datasets and methodology used for mapping and monitoring of flood of Darbhanga and Samastipur districts of Bihar occurred during Aug-Sept, 2017.
Data Used5.1.1 Remote Sensing DataSentinel 1A SAR datasets for the dates 23rd Aug, 4th Sep, 16th Sep and 28th Sep have been used for the study.
Landsat 8 OLI satellite imagery for 21st March 2017 has been used for preparing LULC Map. The data has been downloaded from the USGS website.
The DEM of ALOS PALSAR with spatial resolution 12.5m has been downloaded from Alaska Satellite Facility website for the estimation of flood depth in the study area shown in fig.5.1.
Fig.5.1 Digital Elevation Model of the study areaThe altimetry data of SARAL /AltiKa with repeating period of 35 days has been used for the estimation of water level. Table 5.1 shows the different data for the sensor SARAL /AltiKa used for the study.
Table 5.1 SARAL /AltiKa data for 2 cyclesSARAL / AltiKa
Cycle Track Date
111 170 30-07-2017
112 84 31-08-2017
5.1.2 Ancillary DataCensus map from district census handbook, 2011 has been used as base map for administrative boundary (district and block), roads, railway lines and rivers.
5.2 MethodologyThe methodology involves processing of SAR data & altimeter data, creation of LULC map & flood inundation map and overlay analysis for damage assessment. The Fig.5.2 shows the overall methodology for this study.
Fig. 5.2 Flow Chart of Methodology5.2.1 Land Use Land Cover ClassificationLandsat 8 OLI satellite imagery of 21st March 2017 was downloaded from the USGS website. The study area is covered in two tiles of Landsat 8 OLI. After getting the satellite imageries of the study area stacking of the layers, then mosaicking of the stacked image and finally subsetting of the mosaic image was done in order to get the image of the study area. Fig 5.2 shows the preprocessing techniques used for the LULC classification.
Fig.5.3 Pre-Processing Method11334742476400We have used Supervised Classification Technique around 400 signatures of different classes viz. built up, wasteland, forest, agricultural land and water bodies were taken throughout the area. Maximum Likelihood was taken as parametric rule for classification. Lastly accuracy assessment has also been done for the LULC classification.
5.3.1a Extraction of Permanent Water bodies
The permanent water bodies were extracted from the LULC map. The LULC map was converted into vector data (shapefile) in ArcGIS using conversion tool (Raster to Polygon). In LULC raster map all the water bodies comes under Class 5, so after the conversion all the polygons which represents water bodies will have Grid ID = 5. We select all the polygons with Grid ID = 5 with “select by attributes” tool. After selecting all polygons of Grid ID 5 we have exported the selected features in a separate shapefile. After this we got a shapefile for all the permanent water bodies of the study area.
5.3.1b Damage assessment of agricultural land
Firstly the flood extent layer has been overlaid over LULC map for the identification of flood inundated agricultural area. The total area and flood inundated area has been calculated for the agricultural land by using ‘calculate geometry’ option. This would help in further damage assessment of this class and to take proper steps for mitigation.
5.3.2 Infrastructure MapInfrastructure map is created by using district census map as base map shown in Fig.5.3. The study area consists of two districts Darbhanga and Samastipur. First step was georeferencing of both the raster images of districts map. After georeferencing next step was to digitize the infrastructure i.e. roads and railway lines.
Fig.5.3 Infrastructure Map of the Study Area5.3.3 Extraction of Water using Sentinel 1ADuring 2017 Bihar Floods highest amount of flood water was in mid-August. So, for this study Sentinel 1A satellite imageries of 23 August, 04 September, 16 September and 28 September were used. The spatial resolution of the satellite imagery now resolved at 10m. For the extraction of water from the image SNAP and ArcGIS software were used. Intensity image has been created according to the level of quantization. After that subsetting of the image was done in order to focus on the study area. Next to this radiometric calibration was done in order to get an imagery in which the pixel values truly represent the radar backscatter values of the reflecting surface and also for the comparison of SAR images acquired with different sensors or from the same sensor but at different times, in different modes or processed by different processors. To increase the quality of radar coherent images speckle filtering was done as speckle exists inherently in SAR images. To separate water from non-water class thresholding is done which creates a binary image with value 1 for water and value 0 for non water class. Since the side looking geometry of SAR causes significant distortions due to height differences across track direction which is needed to be rectified hence for this geometric correction was carried out. Then the geometrically corrected image was converted to polygon using conversion tool “Raster to polygon”. Finally a shapefile containing flood water as well as permanent water bodies has been created for further analysis.
5.3.4 Flood MapFrom the Sentinel 1A dataset we have extracted the all the water bodies present on 23 August 2017 which includes both permanent water bodies and flood water and we have also extracted the permanent water bodies from the LULC map.
To get only flood water from the Sentinel 1A extracted water shapefile we have to remove the permanent water body from the shapefile. We will use “Erase tool” to remove the permanent water bodies from the water shapefile of Sentinel 1A.
5.3.5 Water Level Estimation using Altimetry Data The SARAL/ ALtiKa datasets have been used to estimate water level of river and to analyze the change in river water level during flood. Since the SARAL/ ALtiKa tracks were unavailable for our study area hence we have chosen Ganga river, a potential area having flood, as a possible study area for retrieving water level and it also passes through our study area (lower part of Samastipur). The estimation of river water levels from altimeter requires number of range corrections due to atmospheric effects on the microwave pulses. Hence the correction was done in the datasets using BRAT software. The following expression was involved in the calculation of water level:
alt_40hz – ice1_range_40hz – (rad_wet_tropo_corr + model_dry_tropo_corr + pole_tide + iono_corr_gim + solid_earth_tide) – geoid
After executing the above procedure of correction the Brat data is exported as file in ‘*.txt’ format. This Brat exported data is opened in QGIS for fetching the water level information in the river area. The processed altimeter data for different dates shows the water level of the river. This way altimetry datasets could be used for estimating flood water level on the ground.
5.3.6 Flood Water Depth Estimation using DEMIn the current study flood water depth was estimated for four flood polygons. Two point classes were taken one on the boundary and other inside of the polygon, average of both point classes was calculated using “Zonal Statistics as table tool” of ArcGIS for 23rd August 2017 . The difference of the average of two point classes will give the flood depth of that Polygon.
6. RESULTS6.1 Land Use Land Cover ClassificationThe LULC map was prepared using supervised classification with Landsat 8 OLI satellite imagery of 21st March 2017 in ERDAS Imagine software as shown in Fig.6.1.
The study area is classified in five classes on the basis of NRSC Level 1 classification scheme. The LULC classes are agricultural land, forest, water bodies, built-up, wasteland.
Fig.6.1 LULC Map of the Study AreaAgricultural land is the major class, around 83% of the total area comes under this class. This class includes both cultivated (crops and plantations) and fallow land. Wasteland is the second major class which covers 12% of the total area. It includes Barren and Marshy land. Built-up covers both urban and rural settlement. The study area has majorly rural scattered settlement and a few dense urban settlements. Only 1% of the total area comes under this class. Water bodies cover 2% of the total area. It includes rivers, lakes and ponds. Major rivers are Gandak, Ganga and Bagmati. Forest is another class with 2% area. Forest patches are scattered throughout the study area. The area and percentage of the different LULC classes are shown in Table 6.1 and Fig.6.2.
Table 6.1 Land Use Land Cover ClassesS. No. Color LULC class Area(sq. km)
1. Built-up 78.535
2. Wasteland 613.296
3. Forest 110.246
4. Agricultural land 4298.11
5. Water Bodies 99.944
Fig.6.2 Area of the LULC Classes of the Study Area6.1.2 Accuracy Assessment:-For estimating accuracy of the map 200 random points were generated. The overall classification accuracy is 93.5%.
Table 6.2 Accuracy TotalsClass Name Reference Totals Classified Totals Number Correct Producers Accuracy (%) Users Accuracy (%)
Class 1 35 36 34 97.14 94.44
Class 2 10 12 10 100 83.33
Class 3 54 50 49 90.74 98
Class 4 85 85 79 92.94 92.94
Class 5 16 17 15 93.75 88.24
Totals 200 200 187 Table 6.3 Conditional Kappa for each CategoryClass Name Kappa
Class 1 0.9327
Class 2 0.8246
Class 3 0.9726
Class 4 0.8772
Class 5 0.8721
Kappa statistics value is 0.9085
6.2 Flood ExtentSAR data of Sentinel 1A was used to generate flood inundation map for Darbhanga and Samastipur district of Bihar. SAR is an active microwave remote sensing instrument which provides high resolution data in all weather conditions so it helps in accurately delineating the inundated flood area. The inundated area was extracted from the SAR imagery using Threshold method.
right50960480left5093392The flood extent was estimated for four days for the year 2017 on 23rd august, 4th September, 16th September and 28th September shown in Fig.6.3 to Fig.6.6. According to our analysis 23rd August 2017 has the highest flood extent among all the 4 dates.
right3106420Fig.6.5 490 sq. km. (10%) area was inundated in flood water on 16th September 2017.
00Fig.6.5 490 sq. km. (10%) area was inundated in flood water on 16th September 2017.
left3106420Fig.6.3 23rd August imagery has highest flood inundated area which is around 1200 sq. km. which is 24% of the total area.00Fig.6.3 23rd August imagery has highest flood inundated area which is around 1200 sq. km. which is 24% of the total area.
26981153138170Fig.6.6 The flood inundated area on 28th September was 242.48 sq. km. or 5% of the total area.00Fig.6.6 The flood inundated area on 28th September was 242.48 sq. km. or 5% of the total area.left3132455Fig.6.4 On 4th September total flood inundated area is 895 sq. km. or 17% of the total study area.
00Fig.6.4 On 4th September total flood inundated area is 895 sq. km. or 17% of the total study area.
6.3 Damage AssessmentThe damage assessment has been done by overlaying flood extent layer over district, block and infrastructure (road and railway line). This result in estimation of district wise, block wise and infrastructural inundated area.
6.3.1 Estimation of Inundated Area6.3.1a Inundated area District wise
Flood extent map of different dates has been used for estimating inundated area district wise. The district wise flood inundated area for different dates has been shown in Table 6.1.
Table 6.1 District wise Flood Inundated AreaDate
Flood Area (sq. km.)
23/08/2017 977 222
04/09/2017 703 192
16/09/2017 391 100
28/09/2017 181 62
The highest flood extent is on 23rd August which is approx. 40% of the total area of Darbhanga and 8% of Samastipur District. For the comparison between districts receding flood pattern has also been shown in Fig.6.7. The flood water extent decreases which time and 28th September has lowest flood extent.
Fig.6.7 Receding flood for different dates
Fig.6.8 Flood map of 23rd August 2017Source: Bhuvan | ISRO’s Geoportal
As 23rd August has highest flood extent according to the analysis so all the further damage assessment were done for the same day. Flood map of 23rd August 2017 shown in Fig 6.8 validate the result analysed.
5.3.1b Inundated area Block wise
Block with highest amount of inundated area is Darbhanga Block with 106 sq. km inundated area on 23rdAugust. Fig.6.9 shows block wise flood inundated area.
Fig.6.9 Blockwise Flood Inundated Area for 23rd AugustTable 6.2 Blockwise Flood Inundated AreaInundated Area (sq.km) Block Name
0 – 21 Manigachhi, Tardih, Kiratpur, Warisnagar, Shivaji Nagar, Khanpur, Singhia, Rosea, Nasanpur, Bithan, Bibhutpur, Dalsinghsarai, Ujarpur, Sarairanjan, Tajpur Morba, Tajpur, Patori, Mohanpur, Mohiuddinagar
21 – 42 Alinagar, Baheri, Mayaghat, Kalyanpur, Pusa, Samastipur
42 – 64 Gora Bauram, Benpur, Bahadurpur, Keotiranway
64 – 85 Biraul, Jale, Kusheswar Asthan, Kusheswar Asthan Purbi, Vidhyapatinagar
85 – 106 Darbhanga, Hanumangarh, Singhwajia
Blocks with inundated area more or equal to 85 sq. km are Darbhanga, Hanumangarh and Singhwajia as shown in Table 6.2. All the three blocks lies in Darbhanga District.
6.3.2 Infrastructural DamageInfrastructural Damage includes the roads and railway line inundated in the flood water. Roads were further divided into three parts National Highway, State Highway and Major Roads shown in Fig.6.10.
Fig.6.10 Infrastructural Flood Inundation Map for the Study AreaThe estimated flood inundated stretch for different infrastructural commodities are shown in Table 6.3.
Table 6.3 Infrastructural Flood Inundated StretchTotal Stretch Inundated Stretch (%)
NH 105 21 20
SH 30 15 50
Major Road 588 105 17.85
Railway line 208 23 11.05
6.3.3 LULC Damage AssessmentAgricultural Land class has been extracted from the LULC map for overlay analysis and then calculating class wise flood inundated area.
Table 6.4 LULC class wise Flood Inundated AreaS.No. LULC Class Area
(sq. km) Flood Area
(sq. km) Inundated Area (%)
1 Built-up 78.53 3.25 0.06
2 Wasteland 613.29 162.25 3.12
3 Forest 110.24 13.55 0.26
4 Agricultural Land 4298.11 1016.16 19.54
5 Waterbodies 99.94 3.91 0.08
Total 5200.13 1199.15 With 1016 sq. km flood area Agricultural land is the topmost flood affected class. The effects of flood water inundation can be short term such as water logging, crop damage, etc. and can be long term such as loss of nutrients, erosion of top layer of soil, soil salinity and loss of soil productivity. Water logging of agricultural land due to flood also cause economic loss due to crop failure.
26% (162 sq. km) area of Wasteland class is inundated. The estimation of inundated area of Built-up and Forest can differ due to specular and diffused scattering of radiations respectively.
6.4 Water Level Estimation5168901216429
Fig.6.11 SARAL/ AltiKa TracksFrom the Fig.6.11 it is clear that there were 6 tracks of SARAL/ AltiKa available for the study area. The part encircled in figure 6.11 shows water level of the stretch of the Ganga river. Similarly the water levels for different dates are shown in Table 6.5. This shows with time how the water level is changing in the stretch. If there would have been more altimeter tracks for different sensors passing from the study area, the comparative study could be done and hence could be used for further analysis and management.
Table 6.5 Water Level for different datesS.No. Date Water Level (in m)
1. 30-07-2017 41.70
2. 08-08-2017 42.17
3. 15-08-2017 44.69
4. 09-09-2017 46.76
5. 16-09-2017 47.08
6. 25-09-2017 47.40
7. 02-10-2017 49.21
6.5 Flood Water Depth Estimation using DEMPolygon Inner Points
(Average) Outer Points
(Average) Depth (in m)
1 51.67 53.35 1.68
2 47.71 48.5 0.79
3 47 51.71 4.71
4 43.17 44.56 1.39
left86296527966551526770 Table 6.6 Flood Water Depth
6.6 Flood Water Depth00 Table 6.6 Flood Water Depth
6.6 Flood Water DepthThe point classes on the boundary and center of the 4 different flood polygons and the location of selected flood polygons in the study area are shown in Fig.6.12
left3075940Fig.6.12 Flood Depth Estimation
00Fig.6.12 Flood Depth Estimation
The flood water depth of all four polygons are given in Table 6.5. This method can be used to estimate flood water depth if flood extent map and DEM is available.
7. DISCUSSION AND CONCLUSIONFrom results and discussion it is clear that using remote sensing and GIS techniques it is possible to identify the flood inundated area and hence flood depth. In this study an overview of the use of SAR for flood mapping is given and experiences using the SAR data along with key processing elements and important analysis techniques that are used for the extraction of flooded area, spread of flood water and duration of flood dynamics. The Sentinel data is very useful for accurately delineating flood inundated area because it allows acquisition of images independent of the cloud cover and its sensor is very much sensitive to response the land or water surface, rough for land and smooth for water. For the dates 23 Aug, 4 Sep, 16 Sep and 28 Sep the flood extent map has been prepared using single sensor satellite imagery i.e. Sentinel 1A but if we would have adopted the multi sensor approach then flood boundary delineation, duration of flood and hence flood extent could have been analyzed more precisely. Similarly, if we consider long term flood occurred in past, mapping flood inundation and on adding all of them flood hazard map could be generated for the study area. Altimetry data from single sensor provides a few detail as discussed in previous chapter but using multiple sensors data results in getting the complete profile of any water body of the study area which could be helpful for further analysis and management. The depth of the river or any other water reservoir could be calculated if the estimated water level data are there.
8. REFERENCES BIBLIOGRAPHY Bapulu, G. V. & Sinha, R., 2005. GIS in Flood Hazard Mapping: A Case Study of Kosi River Basin, s.l.: GIS Development.
Fua, L. L. & Traon, P. Y. L., 2006. Satellite Altimetry and Ocean Dynamics. Comptes Rendus Geosciences , 338(14-15), pp. 1063-1076 .
Ghosh, S. et al., 2017. The Potential Applications of Satellite Altimetry with SARAL/AltiKa for Indian Inland Waters. The National Academy of Sciences, 87(4), pp. 661-677.
Kanda, M. & Aggarwal, S. K., 2008. National Disaster Management Guidelines: Management of Flood, New Delhi: National Disastr Management Authority.
Lueng, L. & Cazenave, A., 2001. Satellite Altimetery and Earth Sciences : A Handbook of Techniques and Applications. 1 ed. New York: Academic Press.
Lu, Sanyal, J. & X, X., 2004. Application of Remote Sensing in Flood Management with Special Reference to Monsoon Asia: A Review. Natural Hazards, Volume 33, pp. 283-301.
Mohamed, T. & Gasmelsied, 2011. Handbook of Research on Hydroinformatics: Technologies, Theories and Applications. New York: Information Science Reference.
Sahu, S., 2013. Ground Water Information Booklet, s.l.: Central Ground water Board.
Shukla, R. R., 2013. Ground Water Information Booklet, s.l.: Central Ground Water Board.
Smith, L. C., 1997. Hydrological Processes, Volume 11, pp. 1427 – 1439.