Flood is one of the most frequent, pervasive and devastating natural hazards in the world now-a-days, particularly flooding in urban areas is an inevitable problem for many cities in Asia. In Bangladesh, Dinajpur Sadar has serious problems related to urban flooding. Bangladesh is one of the most flood prone countries in the world, which is situated in south Asian sub-continent. A location map of Bangladesh is given in fig.2.1. Because of its unique geographical location and to1pography, flood of different magnitudes and types occurs every year. During the last half century at least 8 nos. of extreme flood events occurred affecting 50% of land area. Since early sixties of the last century the country has adopted different kinds of measures for flood management with mixed experiences (Hossain, 2003).
One of the major challenges during flood is to get an overall view of the incident with accurate extent of the affected area and, to predict the possible developments. Using traditional methods such as ground survey and aerial observation, flood mapping is time consuming, expensive and need to be involved skilled persons. Moreover, if the occurrence is extensive then it is very difficult to monitor the flood event accurately and very quickly. On the other hand, due to bad weather conditions it is not possible to acquire timely aerial observations also.
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Now-a-days, the availability of multiple satellite data can be used as an effective alternative to monitor flood situation and extent in the particular area (Brivio et al., 2002). However, in monsoon climate region, huge cloud cover, rains and haze during and post flood events can represent a strong constraint to the utilization of optical remotely sensed data. In contrast, micro-wave remote sensing equipped with synthetic aperture radar (SAR) system, because of their exclusive cloud, rain and haze penetration capacity, offers a primary tool for real-time assessment of flooded areas. Beside the penetration capacity of SAR data, the most important advantage of using SAR data is that land and water contrast can be easily distinguished (Dewan et al., 2006). SAR sensors are able to detect flooding because flat surfaces reflect (acts as a specular reflector) the signal away from the sensor, decreasing the amount of returned radiation (Gan et al., 2012). This causes relatively dark pixels in radar data for water areas which contrast with non-water areas. For the analyses of the temporal and spatial dynamics of the disaster used Sentinel-1 Synthetic Aperture Radar (SAR) data due to its systematic frequent acquisition. A dataset of pre-event and post-event Sentinel-1 images was acquired. Flooded areas were extracted with threshold, random forest and deep learning approaches (Bayik et al., 2018).
On the other hand, satellite data-based information during flood, pre-flood and post-flood along with GIS and ground information, flood damages can also be estimated (Rahman, 2006). Based on the duration of flooding, magnitude of the flood, area affected, types of land use features etc., flood damage map can be prepared. Besides, combined with high resolution digital elevation model (DEM) of the flooded area and surrounding, the flood depth can quite well be estimated from the flooded maps (Rahman and Saha, 2007; Voigt et al., 2008). Therefore, this study was initiated to evaluate the advantage of using Sentinel-1 data in monitoring of flood water propagation in a flood prone area.
Water body extraction by using remote sensing has been the most important method in the investigation of water resources, flood hazard prediction assessment and water planning with fast and accurate effectiveness. Multiple methods including unsupervised classification, supervised classification, single-band threshold, inter spectrum relation method and water index method (normalized difference water index, modified normalized difference water index, and new water index) are analyzed (Haibo et al., 2011).
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