Bangladesh is one of the most flood affected country in the world. The frequency, intensity and duration of floods has been increased during last few decades. Due to increased population settlements in floodplains and irregular development damage of infrastructure, crop and property has increased creating severe impact on lives and livelihood. Understanding the severity and identification of extent and types of flood damage is highly important to plan effective response. The aim of this study was to develop appropriate methodology to determine extent of flood and damaged areas in near real time basis to support operational response. We have used Sentinel-1 synthetic aperture radar (SAR) images to generate flood extend data for the year 2017.

Bangladesh is one of the most flood affected country in the world. The frequency, intensity and duration of floods has been increased during last few decades. Due to increased population settlements in floodplains and irregular development damage of infrastructure, crop and property has increased creating severe impact on lives and livelihood. Understanding the severity and identification of extent and types of flood damage is highly important to plan effective response. The aim of this study was to develop appropriate methodology to determine extent of flood and damaged areas in near real time basis to support operational response. We have used Sentinel-1 synthetic aperture radar (SAR) images to generate flood extend data for the year 2017.

Nepal is one of the most flood affected country in the world. The frequency, intensity and duration of floods has been increased during last few decades. Due to increased population settlements in floodplains and irregular development damage of infrastructure, crop and property has increased creating severe impact on lives and livelihood. Understanding the severity and identification of extent and types of flood damage is highly important to plan effective response. The aim of this study was to develop appropriate methodology to determine extent of flood and damaged areas in near real time basis to support operational response. We have used Sentinel-1 synthetic aperture radar (SAR) images to generate flood extend data for the year 2017.

Nepal is one of the most flood affected country in the world. The frequency, intensity and duration of floods has been increased during last few decades. Due to increased population settlements in floodplains and irregular development damage of infrastructure, crop and property has increased creating severe impact on lives and livelihood. Understanding the severity and identification of extent and types of flood damage is highly important to plan effective response. The aim of this study was to develop appropriate methodology to determine extent of flood and damaged areas in near real time basis to support operational response. We have used Sentinel-1 synthetic aperture radar (SAR) images to generate flood extend data for the year 2017.

The Poverty and Vulnerability Assessment Tool (PVAT) dataset was developed to capture the micro-level perspective of people's experiences with poverty and vulnerability and to monitor poverty and vulnerability trends on the ground. The assessment was based on a standardized questionnaire that has been developed to assess, describe, and analyse the situation of poor and vulnerable people in the Nepal. This will lead to an enhanced understanding of multiple aspects of livelihood assets, structures, and the processes that underpin such assets. It will also help determine levels of access, terms of exchange, and returns from livelihood assets as well as livelihood strategies and their outcomes for the poor. PVAT was conducted twice in 2011 and 2012. Sankhuwashava,Terthum, Saptari, Gorkha, Jajarkot, Kailali, Bajhang and Sindhupalchok in 2011. Taplejung, Bhojpur, Solukhumbu, Sindhuli, Rukum, Dailekh, Mugu and Darchula in 2012.

A comparison between male and female population residing in the Koshi Basin Districts in various parameters revealing the trends in marriage, population, life expectancy, migration, ethnicity and other parameters.

A comparison between male and female population residing in the Koshi Basin Districts in various parameters shows more male population who have higher literacy rate, higher rate of land and house ownership, involved in higher income generating jobs as well as 76% of men are also head of household compared to female despite the fact that there are equal population women and men.

The QuickBird image showing Jumla, Nepal was acquired on 28 September 2010.