Flood is the most devastating disaster in the present world which causes damage to environmental, social, economical and human lives at about 43% of all natural disasters. There are many flood hazard occurs in Bangladesh during the 19 th century and 20 th century in the different regions. These flood hazards have more catastrophic damages of huge area within human lives and other necessary properties of Bangladesh. The first step of flood management is to evaluate the area which is under threat of flood disaster. In this study here showed the importance of Remote Sensing (RS) data and Geographic Information System (GIS) tools to manage the flood related problems. Remote Sensing (RS) data and Geographic Information System (GIS) provide a lot of information to flood disaster management. ArcView GIS software tools are used for digitizing the base map and to create a flood risk zone of Kurigram, Bangladesh where images of remote sensing can be helped to determine the flood inundation areas. The integrated application of RS and GIS techniques for monitoring and flood mapping provides information for the decision makers. The study also grows attentions the need of cost-efficient methodology by creating a flood vulnerable map of Bangladesh.

Rangpur is one of the fastest growing cities of Bangladesh with a dense population. Being the headquarter of a division in Bangladesh, it is experiencing multi-dimensional problems such as over urbanization, traffic congestion, water logging, and solid waste disposal. Rangpur is a sheer example of having poor legislative actions, inefficient management and lack of public awareness, which leads the urbanization to an unplanned and resource consuming development. This study presents an integrated study of land use pattern in Rangpur City, Bangladesh, by using Geographical Information Systems (GIS) and Remote Sensing (RS). The data sources used in this study were Landsat Thematic Mapper (TM) and a Landsat Enhanced Thematic Mapper Plus (ETM+) images taken in 1989, 2000 and 2014, respectively. All images were geometrically and radiometrically corrected and the change detection methods were performed. Then, supervised maximum likelihood classification was used as a cross classification to detect change. The study area was classified into six categories on the basis of field study, geographical conditions, and remote sensing data. The remotely detected land use change from 1989 to 2014 shows that Rangpur is gradually changing, as planted trees, open spaces, low land and Permanent water sources have been transformed into built-up areas.

An attempt is made to study mathematical properties of singular value decomposition (SVD) and its data exploring capacity and to apply them to make exploratory type clustering for 10 climatic variables and thirty weather stations in Bangladesh using a newly developed graphical technique. Findings in SVD and Robust singular value decomposition (RSVD) based graphs are compared with that of classical K-means cluster analysis, its robust version, partition by medoids (PAM) and classical factor analysis, and the comparison clearly demonstrates the advantage of SVD over its competitors. Lastly the method is tested on well known Hawkins-Bradu-Kass (1984) data.

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